For using the igraph C library
These functions usually calculate some structural property of a graph, like its diameter, the degree of the nodes, etc.
int igraph_are_connected(const igraph_t *graph, igraph_integer_t v1, igraph_integer_t v2, igraph_bool_t *res);
Arguments:

The graph object. 

The first vertex. 

The second vertex. 

Boolean, 
Returns:
The error code 
The function is of course symmetric for undirected graphs.
Time complexity: O( min(log(d1), log(d2)) ),
d1 is the (out)degree of v1
and d2 is the (in)degree of v2
.
igraph_shortest_paths
— The length of the shortest paths between vertices.igraph_shortest_paths_dijkstra
— Weighted shortest paths from some sources.igraph_shortest_paths_bellman_ford
— Weighted shortest paths from some sources allowing negative weights.igraph_shortest_paths_johnson
— Calculate shortest paths from some sources using Johnson's algorithm.igraph_get_shortest_paths
— Calculates the shortest paths from/to one vertex.igraph_get_shortest_path
— Shortest path from one vertex to another one.igraph_get_shortest_paths_dijkstra
— Calculates the weighted shortest paths from/to one vertex.igraph_get_shortest_path_dijkstra
— Weighted shortest path from one vertex to another one.igraph_get_all_shortest_paths
— Finds all shortest paths (geodesics) from a vertex to all other vertices.igraph_get_all_shortest_paths_dijkstra
— Finds all shortest paths (geodesics) from a vertex to all other vertices.igraph_get_all_simple_paths
— List all simple paths from one sourceigraph_average_path_length
— Calculates the average shortest path length between all vertex pairs.igraph_path_length_hist
— Create a histogram of all shortest path lengths.igraph_diameter
— Calculates the diameter of a graph (longest geodesic).igraph_diameter_dijkstra
— Weighted diameter using Dijkstra's algorithm, nonnegative weights only.igraph_girth
— The girth of a graph is the length of the shortest circle in it.igraph_eccentricity
— Eccentricity of some verticesigraph_radius
— Radius of a graph
int igraph_shortest_paths(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t from, const igraph_vs_t to, igraph_neimode_t mode);
Arguments:

The graph object. 


The result of the calculation, a matrix. A pointer to an
initialized matrix, to be more precise. The matrix will be
resized if needed. It will have the same
number of rows as the length of the 


Vector of the vertex ids for which the path length calculations are done. 


Vector of the vertex ids to which the path length calculations are done. It is not allowed to have duplicated vertex ids here. 


The type of shortest paths to be used for the calculation in directed graphs. Possible values:

Returns:
Error code:

Time complexity: O(n(V+E)), n is the number of vertices to calculate, V and E are the number of vertices and edges in the graph.
See also:

int igraph_shortest_paths_dijkstra(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t from, const igraph_vs_t to, const igraph_vector_t *weights, igraph_neimode_t mode);
This function is Dijkstra's algorithm to find the weighted shortest paths to all vertices from a single source. (It is run independently for the given sources.) It uses a binary heap for efficient implementation.
Arguments:

The input graph, can be directed. 

The result, a matrix. A pointer to an initialized matrix
should be passed here. The matrix will be resized as needed.
Each row contains the distances from a single source, to the
vertices given in the 

The source vertices. 

The target vertices. It is not allowed to include a vertex twice or more. 

The edge weights. They must be all nonnegative for
Dijkstra's algorithm to work. An error code is returned if there
is a negative edge weight in the weight vector. If this is a null
pointer, then the
unweighted version, 

For directed graphs; whether to follow paths along edge
directions ( 
Returns:
Error code. 
Time complexity: O(s*ElogE+V), where V is the number of vertices, E the number of edges and s the number of sources.
See also:

Example 13.1. File examples/simple/dijkstra.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20082012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int print_matrix(const igraph_matrix_t *m) { long int nrow = igraph_matrix_nrow(m); long int ncol = igraph_matrix_ncol(m); long int i, j; igraph_real_t val; for (i = 0; i < nrow; i++) { printf("%li:", i); for (j = 0; j < ncol; j++) { val = MATRIX(*m, i, j); if (igraph_is_inf(val)) { if (val < 0) { printf("inf"); } else { printf(" inf"); } } else { printf(" %3.0f", val); } } printf("\n"); } return 0; } int main() { igraph_t g; igraph_vector_t weights; igraph_real_t weights_data[] = { 0, 2, 1, 0, 5, 2, 1, 1, 0, 2, 2, 8, 1, 1, 3, 1, 1, 4, 2, 1 }; igraph_matrix_t res; igraph_small(&g, 10, IGRAPH_DIRECTED, 0, 1, 0, 2, 0, 3, 1, 2, 1, 4, 1, 5, 2, 3, 2, 6, 3, 2, 3, 6, 4, 5, 4, 7, 5, 6, 5, 8, 5, 9, 7, 5, 7, 8, 8, 9, 5, 2, 2, 1, 1); igraph_vector_view(&weights, weights_data, sizeof(weights_data) / sizeof(igraph_real_t)); igraph_matrix_init(&res, 0, 0); igraph_shortest_paths_dijkstra(&g, &res, igraph_vss_all(), igraph_vss_all(), &weights, IGRAPH_OUT); print_matrix(&res); igraph_matrix_destroy(&res); igraph_destroy(&g); return 0; }
int igraph_shortest_paths_bellman_ford(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t from, const igraph_vs_t to, const igraph_vector_t *weights, igraph_neimode_t mode);
This function is the BellmanFord algorithm to find the weighted
shortest paths to all vertices from a single source. (It is run
independently for the given sources.). If there are no negative
weights, you are better off with igraph_shortest_paths_dijkstra()
.
Arguments:

The input graph, can be directed. 

The result, a matrix. A pointer to an initialized matrix
should be passed here, the matrix will be resized if needed.
Each row contains the distances from a single source, to all
vertices in the graph, in the order of vertex ids. For unreachable
vertices the matrix contains 

The source vertices. 

The edge weights. There mustn't be any closed loop in
the graph that has a negative total weight (since this would allow
us to decrease the weight of any path containing at least a single
vertex of this loop infinitely). If this is a null pointer, then the
unweighted version, 

For directed graphs; whether to follow paths along edge
directions ( 
Returns:
Error code. 
Time complexity: O(s*E*V), where V is the number of vertices, E the number of edges and s the number of sources.
See also:

Example 13.2. File examples/simple/bellman_ford.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20082012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int print_matrix(const igraph_matrix_t *m) { long int nrow = igraph_matrix_nrow(m); long int ncol = igraph_matrix_ncol(m); long int i, j; igraph_real_t val; for (i = 0; i < nrow; i++) { printf("%li:", i); for (j = 0; j < ncol; j++) { val = MATRIX(*m, i, j); if (igraph_is_inf(val)) { if (val < 0) { printf("inf"); } else { printf(" inf"); } } else { printf(" %3.0f", val); } } printf("\n"); } return 0; } int main() { igraph_t g; igraph_vector_t weights; igraph_real_t weights_data_0[] = { 0, 2, 1, 0, 5, 2, 1, 1, 0, 2, 2, 8, 1, 1, 3, 1, 1, 4, 2, 1 }; igraph_real_t weights_data_1[] = { 6, 7, 8, 4, 2, 3, 9, 2, 7 }; igraph_real_t weights_data_2[] = { 6, 7, 2, 4, 2, 3, 9, 2, 7 }; igraph_matrix_t res; /* Graph with only positive weights */ igraph_small(&g, 10, IGRAPH_DIRECTED, 0, 1, 0, 2, 0, 3, 1, 2, 1, 4, 1, 5, 2, 3, 2, 6, 3, 2, 3, 6, 4, 5, 4, 7, 5, 6, 5, 8, 5, 9, 7, 5, 7, 8, 8, 9, 5, 2, 2, 1, 1); igraph_vector_view(&weights, weights_data_0, sizeof(weights_data_0) / sizeof(igraph_real_t)); igraph_matrix_init(&res, 0, 0); igraph_shortest_paths_bellman_ford(&g, &res, igraph_vss_all(), igraph_vss_all(), &weights, IGRAPH_OUT); print_matrix(&res); igraph_matrix_destroy(&res); igraph_destroy(&g); printf("\n"); /***************************************/ /* Graph with negative weights */ igraph_small(&g, 5, IGRAPH_DIRECTED, 0, 1, 0, 3, 1, 3, 1, 4, 2, 1, 3, 2, 3, 4, 4, 0, 4, 2, 1); igraph_vector_view(&weights, weights_data_1, sizeof(weights_data_1) / sizeof(igraph_real_t)); igraph_matrix_init(&res, 0, 0); igraph_shortest_paths_bellman_ford(&g, &res, igraph_vss_all(), igraph_vss_all(), &weights, IGRAPH_OUT); print_matrix(&res); /***************************************/ /* Same graph with negative loop */ igraph_set_error_handler(igraph_error_handler_ignore); igraph_vector_view(&weights, weights_data_2, sizeof(weights_data_2) / sizeof(igraph_real_t)); if (igraph_shortest_paths_bellman_ford(&g, &res, igraph_vss_all(), igraph_vss_all(), &weights, IGRAPH_OUT) != IGRAPH_ENEGLOOP) { return 1; } igraph_matrix_destroy(&res); igraph_destroy(&g); if (!IGRAPH_FINALLY_STACK_EMPTY) { return 1; } return 0; }
int igraph_shortest_paths_johnson(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t from, const igraph_vs_t to, const igraph_vector_t *weights);
See Wikipedia at http://en.wikipedia.org/wiki/Johnson's_algorithm for Johnson's algorithm. This algorithm works even if the graph contains negative edge weights, and it is worth using it if we calculate the shortest paths from many sources.
If no edge weights are supplied, then the unweighted
version, igraph_shortest_paths()
is called.
If all the supplied edge weights are nonnegative,
then Dijkstra's algorithm is used by calling
igraph_shortest_paths_dijkstra()
.
Arguments:

The input graph, typically it is directed. 

Pointer to an initialized matrix, the result will be stored here, one line for each source vertex, one column for each target vertex. 

The source vertices. 

The target vertices. It is not allowed to include a vertex twice or more. 

Optional edge weights. If it is a nullpointer, then
the unweighted breadthfirst search based 
Returns:
Error code. 
Time complexity: O(sVlogV+VE), V and E are the number of vertices and edges, s is the number of source vertices.
See also:

int igraph_get_shortest_paths(const igraph_t *graph, igraph_vector_ptr_t *vertices, igraph_vector_ptr_t *edges, igraph_integer_t from, const igraph_vs_t to, igraph_neimode_t mode, igraph_vector_long_t *predecessors, igraph_vector_long_t *inbound_edges);
If there is more than one geodesic between two vertices, this function gives only one of them.
Arguments:

The graph object. 


The result, the ids of the vertices along the paths. This is a pointer vector, each element points to a vector object. These should be initialized before passing them to the function, which will properly clear and/or resize them and fill the ids of the vertices along the geodesics from/to the vertices. Supply a null pointer here if you don't need these vectors. 


The result, the ids of the edges along the paths. This is a pointer vector, each element points to a vector object. These should be initialized before passing them to the function, which will properly clear and/or resize them and fill the ids of the vertices along the geodesics from/to the vertices. Supply a null pointer here if you don't need these vectors. 


The id of the vertex from/to which the geodesics are calculated. 


Vertex sequence with the ids of the vertices to/from which the shortest paths will be calculated. A vertex might be given multiple times. 


The type of shortest paths to be used for the calculation in directed graphs. Possible values:



A pointer to an initialized igraph vector or null.
If not null, a vector containing the predecessor of each vertex in
the single source shortest path tree is returned here. The
predecessor of vertex i in the tree is the vertex from which vertex i
was reached. The predecessor of the start vertex (in the 


A pointer to an initialized igraph vector or null.
If not null, a vector containing the inbound edge of each vertex in
the single source shortest path tree is returned here. The
inbound edge of vertex i in the tree is the edge via which vertex i
was reached. The start vertex and vertices that were not reached
during the search will have 1 in the corresponding entry of the
vector. Note that the search terminates if all the vertices in

Returns:
Error code:

Time complexity: O(V+E), V is the number of vertices, E the number of edges in the graph.
See also:

Example 13.3. File examples/simple/igraph_get_shortest_paths.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdlib.h> void print_vector(igraph_vector_t *v) { long int i, l = igraph_vector_size(v); for (i = 0; i < l; i++) { printf(" %li", (long int) VECTOR(*v)[i]); } printf("\n"); } int check_evecs(const igraph_t *graph, const igraph_vector_ptr_t *vecs, const igraph_vector_ptr_t *evecs, int error_code) { igraph_bool_t directed = igraph_is_directed(graph); long int i, n = igraph_vector_ptr_size(vecs); if (igraph_vector_ptr_size(evecs) != n) { exit(error_code + 1); } for (i = 0; i < n; i++) { igraph_vector_t *vvec = VECTOR(*vecs)[i]; igraph_vector_t *evec = VECTOR(*evecs)[i]; long int j, n2 = igraph_vector_size(evec); if (igraph_vector_size(vvec) == 0 && n2 == 0) { continue; } if (igraph_vector_size(vvec) != n2 + 1) { exit(error_code + 2); } for (j = 0; j < n2; j++) { long int edge = VECTOR(*evec)[j]; long int from = VECTOR(*vvec)[j]; long int to = VECTOR(*vvec)[j + 1]; if (directed) { if (from != IGRAPH_FROM(graph, edge)  to != IGRAPH_TO (graph, edge)) { exit(error_code); } } else { long int from2 = IGRAPH_FROM(graph, edge); long int to2 = IGRAPH_TO(graph, edge); long int min1 = from < to ? from : to; long int max1 = from < to ? to : from; long int min2 = from2 < to2 ? from2 : to2; long int max2 = from2 < to2 ? to2 : from2; if (min1 != min2  max1 != max2) { exit(error_code + 3); } } } } return 0; } int main() { igraph_t g; igraph_vector_ptr_t vecs, evecs; igraph_vector_long_t pred, inbound; long int i; igraph_vs_t vs; igraph_ring(&g, 10, IGRAPH_DIRECTED, 0, 1); igraph_vector_ptr_init(&vecs, 5); igraph_vector_ptr_init(&evecs, 5); igraph_vector_long_init(&pred, 0); igraph_vector_long_init(&inbound, 0); for (i = 0; i < igraph_vector_ptr_size(&vecs); i++) { VECTOR(vecs)[i] = calloc(1, sizeof(igraph_vector_t)); igraph_vector_init(VECTOR(vecs)[i], 0); VECTOR(evecs)[i] = calloc(1, sizeof(igraph_vector_t)); igraph_vector_init(VECTOR(evecs)[i], 0); } igraph_vs_vector_small(&vs, 1, 3, 5, 2, 1, 1); igraph_get_shortest_paths(&g, &vecs, &evecs, 0, vs, IGRAPH_OUT, &pred, &inbound); check_evecs(&g, &vecs, &evecs, 10); for (i = 0; i < igraph_vector_ptr_size(&vecs); i++) { print_vector(VECTOR(vecs)[i]); igraph_vector_destroy(VECTOR(vecs)[i]); free(VECTOR(vecs)[i]); igraph_vector_destroy(VECTOR(evecs)[i]); free(VECTOR(evecs)[i]); } igraph_vector_long_print(&pred); igraph_vector_long_print(&inbound); igraph_vector_ptr_destroy(&vecs); igraph_vector_ptr_destroy(&evecs); igraph_vector_long_destroy(&pred); igraph_vector_long_destroy(&inbound); igraph_vs_destroy(&vs); igraph_destroy(&g); if (!IGRAPH_FINALLY_STACK_EMPTY) { return 1; } return 0; }
int igraph_get_shortest_path(const igraph_t *graph, igraph_vector_t *vertices, igraph_vector_t *edges, igraph_integer_t from, igraph_integer_t to, igraph_neimode_t mode);
Calculates and returns a single unweighted shortest path from a given vertex to another one. If there are more than one shortest paths between the two vertices, then an arbitrary one is returned.
This function is a wrapper to igraph_get_shortest_paths()
, for the special case when only one
target vertex is considered.
Arguments:

The input graph, it can be directed or undirected. Directed paths are considered in directed graphs. 

Pointer to an initialized vector or a null pointer. If not a null pointer, then the vertex ids along the path are stored here, including the source and target vertices. 

Pointer to an uninitialized vector or a null pointer. If not a null pointer, then the edge ids along the path are stored here. 

The id of the source vertex. 

The id of the target vertex. 

A constant specifying how edge directions are
considered in directed graphs. Valid modes are:

Returns:
Error code. 
Time complexity: O(V+E), linear in the number of vertices and edges in the graph.
See also:

int igraph_get_shortest_paths_dijkstra(const igraph_t *graph, igraph_vector_ptr_t *vertices, igraph_vector_ptr_t *edges, igraph_integer_t from, igraph_vs_t to, const igraph_vector_t *weights, igraph_neimode_t mode, igraph_vector_long_t *predecessors, igraph_vector_long_t *inbound_edges);
If there is more than one path with the smallest weight between two vertices, this function gives only one of them.
Arguments:

The graph object. 


The result, the ids of the vertices along the paths.
This is a pointer vector, each element points to a vector
object. These should be initialized before passing them to
the function, which will properly clear and/or resize them
and fill the ids of the vertices along the geodesics from/to
the vertices. Supply a null pointer here if you don't need
these vectors. Normally, either this argument, or the 


The result, the ids of the edges along the paths.
This is a pointer vector, each element points to a vector
object. These should be initialized before passing them to
the function, which will properly clear and/or resize them
and fill the ids of the vertices along the geodesics from/to
the vertices. Supply a null pointer here if you don't need
these vectors. Normally, either this argument, or the 


The id of the vertex from/to which the geodesics are calculated. 


Vertex sequence with the ids of the vertices to/from which the shortest paths will be calculated. A vertex might be given multiple times. 


a vector holding the edge weights. All weights must be positive. 


The type of shortest paths to be use for the calculation in directed graphs. Possible values:



A pointer to an initialized igraph vector or null.
If not null, a vector containing the predecessor of each vertex in
the single source shortest path tree is returned here. The
predecessor of vertex i in the tree is the vertex from which vertex i
was reached. The predecessor of the start vertex (in the 


A pointer to an initialized igraph vector or null.
If not null, a vector containing the inbound edge of each vertex in
the single source shortest path tree is returned here. The
inbound edge of vertex i in the tree is the edge via which vertex i
was reached. The start vertex and vertices that were not reached
during the search will have 1 in the corresponding entry of the
vector. Note that the search terminates if all the vertices in

Returns:
Error code:

Time complexity: O(ElogE+V), where V is the number of vertices and E is the number of edges
See also:

Example 13.4. File examples/simple/igraph_get_shortest_paths_dijkstra.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdlib.h> void print_vector(igraph_vector_t *v) { long int i, l = igraph_vector_size(v); for (i = 0; i < l; i++) { printf(" %li", (long int) VECTOR(*v)[i]); } printf("\n"); } int check_evecs(const igraph_t *graph, const igraph_vector_ptr_t *vecs, const igraph_vector_ptr_t *evecs, int error_code) { igraph_bool_t directed = igraph_is_directed(graph); long int i, n = igraph_vector_ptr_size(vecs); if (igraph_vector_ptr_size(evecs) != n) { exit(error_code + 1); } for (i = 0; i < n; i++) { igraph_vector_t *vvec = VECTOR(*vecs)[i]; igraph_vector_t *evec = VECTOR(*evecs)[i]; long int j, n2 = igraph_vector_size(evec); if (igraph_vector_size(vvec) == 0 && n2 == 0) { continue; } if (igraph_vector_size(vvec) != n2 + 1) { exit(error_code + 2); } for (j = 0; j < n2; j++) { long int edge = VECTOR(*evec)[j]; long int from = VECTOR(*vvec)[j]; long int to = VECTOR(*vvec)[j + 1]; if (directed) { if (from != IGRAPH_FROM(graph, edge)  to != IGRAPH_TO (graph, edge)) { exit(error_code); } } else { long int from2 = IGRAPH_FROM(graph, edge); long int to2 = IGRAPH_TO(graph, edge); long int min1 = from < to ? from : to; long int max1 = from < to ? to : from; long int min2 = from2 < to2 ? from2 : to2; long int max2 = from2 < to2 ? to2 : from2; if (min1 != min2  max1 != max2) { exit(error_code + 3); } } } } return 0; } int check_pred_inbound(const igraph_t* graph, const igraph_vector_long_t* pred, const igraph_vector_long_t* inbound, int start, int error_code) { long int i, n = igraph_vcount(graph); if (igraph_vector_long_size(pred) != n  igraph_vector_long_size(inbound) != n) { exit(error_code); } if (VECTOR(*pred)[start] != start  VECTOR(*inbound)[start] != 1) { exit(error_code + 1); } for (i = 0; i < n; i++) { if (VECTOR(*pred)[i] == 1) { if (VECTOR(*inbound)[i] != 1) { exit(error_code + 2); } } else if (VECTOR(*pred)[i] == i) { if (i != start) { exit(error_code + 3); } if (VECTOR(*inbound)[i] != 1) { exit(error_code + 4); } } else { long int eid = VECTOR(*inbound)[i]; long int u = IGRAPH_FROM(graph, eid), v = IGRAPH_TO(graph, eid); if (v != i && !igraph_is_directed(graph)) { long int dummy = u; u = v; v = dummy; } if (v != i) { exit(error_code + 5); } else if (u != VECTOR(*pred)[i]) { exit(error_code + 6); } } } return 0; } int main() { igraph_t g; igraph_vector_ptr_t vecs, evecs; igraph_vector_long_t pred, inbound; long int i; igraph_real_t weights[] = { 1, 2, 3, 4, 5, 1, 1, 1, 1, 1 }; igraph_real_t weights2[] = { 0, 2, 1, 0, 5, 2, 1, 1, 0, 2, 2, 8, 1, 1, 3, 1, 1, 4, 2, 1 }; igraph_vector_t weights_vec; igraph_vs_t vs; /* Simple ring graph without weights */ igraph_ring(&g, 10, IGRAPH_UNDIRECTED, 0, 1); igraph_vector_ptr_init(&vecs, 6); igraph_vector_ptr_init(&evecs, 6); igraph_vector_long_init(&pred, 0); igraph_vector_long_init(&inbound, 0); for (i = 0; i < igraph_vector_ptr_size(&vecs); i++) { VECTOR(vecs)[i] = calloc(1, sizeof(igraph_vector_t)); igraph_vector_init(VECTOR(vecs)[i], 0); VECTOR(evecs)[i] = calloc(1, sizeof(igraph_vector_t)); igraph_vector_init(VECTOR(evecs)[i], 0); } igraph_vs_vector_small(&vs, 0, 1, 3, 5, 2, 1, 1); igraph_get_shortest_paths_dijkstra(&g, /*vertices=*/ &vecs, /*edges=*/ &evecs, /*from=*/ 0, /*to=*/ vs, /*weights=*/ 0, /*mode=*/ IGRAPH_OUT, /*predecessors=*/ &pred, /*inbound_edges=*/ &inbound); check_evecs(&g, &vecs, &evecs, 10); check_pred_inbound(&g, &pred, &inbound, /* from= */ 0, 40); for (i = 0; i < igraph_vector_ptr_size(&vecs); i++) { print_vector(VECTOR(vecs)[i]); } /* Same ring, but with weights */ igraph_vector_view(&weights_vec, weights, sizeof(weights) / sizeof(igraph_real_t)); igraph_get_shortest_paths_dijkstra(&g, /*vertices=*/ &vecs, /*edges=*/ &evecs, /*from=*/ 0, /*to=*/ vs, &weights_vec, IGRAPH_OUT, /*predecessors=*/ &pred, /*inbound_edges=*/ &inbound); check_evecs(&g, &vecs, &evecs, 20); check_pred_inbound(&g, &pred, &inbound, /* from= */ 0, 50); for (i = 0; i < igraph_vector_ptr_size(&vecs); i++) { print_vector(VECTOR(vecs)[i]); } igraph_destroy(&g); /* More complicated example */ igraph_small(&g, 10, IGRAPH_DIRECTED, 0, 1, 0, 2, 0, 3, 1, 2, 1, 4, 1, 5, 2, 3, 2, 6, 3, 2, 3, 6, 4, 5, 4, 7, 5, 6, 5, 8, 5, 9, 7, 5, 7, 8, 8, 9, 5, 2, 2, 1, 1); igraph_vector_view(&weights_vec, weights2, sizeof(weights2) / sizeof(igraph_real_t)); igraph_get_shortest_paths_dijkstra(&g, /*vertices=*/ &vecs, /*edges=*/ &evecs, /*from=*/ 0, /*to=*/ vs, &weights_vec, IGRAPH_OUT, /*predecessors=*/ &pred, /*inbound_edges=*/ &inbound); check_evecs(&g, &vecs, &evecs, 30); check_pred_inbound(&g, &pred, &inbound, /* from= */ 0, 60); for (i = 0; i < igraph_vector_ptr_size(&vecs); i++) { print_vector(VECTOR(vecs)[i]); igraph_vector_destroy(VECTOR(vecs)[i]); free(VECTOR(vecs)[i]); igraph_vector_destroy(VECTOR(evecs)[i]); free(VECTOR(evecs)[i]); } igraph_vector_ptr_destroy(&vecs); igraph_vector_ptr_destroy(&evecs); igraph_vector_long_destroy(&pred); igraph_vector_long_destroy(&inbound); igraph_vs_destroy(&vs); igraph_destroy(&g); if (!IGRAPH_FINALLY_STACK_EMPTY) { return 1; } return 0; }
int igraph_get_shortest_path_dijkstra(const igraph_t *graph, igraph_vector_t *vertices, igraph_vector_t *edges, igraph_integer_t from, igraph_integer_t to, const igraph_vector_t *weights, igraph_neimode_t mode);
Calculates a single (positively) weighted shortest path from a single vertex to another one, using Dijkstra's algorithm.
This function is a special case (and a wrapper) to
igraph_get_shortest_paths_dijkstra()
.
Arguments:

The input graph, it can be directed or undirected. 

Pointer to an initialized vector or a null pointer. If not a null pointer, then the vertex ids along the path are stored here, including the source and target vertices. 

Pointer to an uninitialized vector or a null pointer. If not a null pointer, then the edge ids along the path are stored here. 

The id of the source vertex. 

The id of the target vertex. 

Vector of edge weights, in the order of edge ids. They must be nonnegative, otherwise the algorithm does not work. 

A constant specifying how edge directions are
considered in directed graphs. 
Returns:
Error code. 
Time complexity: O(ElogE+V), V is the number of vertices, E is the number of edges in the graph.
See also:

int igraph_get_all_shortest_paths(const igraph_t *graph, igraph_vector_ptr_t *res, igraph_vector_t *nrgeo, igraph_integer_t from, const igraph_vs_t to, igraph_neimode_t mode);
Arguments:

The graph object. 


Pointer to an initialized pointer vector, the result
will be stored here in igraph_vector_t objects. Each vector
object contains the vertices along a shortest path from 


Pointer to an initialized igraph_vector_t object or
NULL. If not NULL the number of shortest paths from 


The id of the vertex from/to which the geodesics are calculated. 


Vertex sequence with the ids of the vertices to/from which the shortest paths will be calculated. A vertex might be given multiple times. 


The type of shortest paths to be use for the calculation in directed graphs. Possible values:

Returns:
Error code:

Added in version 0.2.
Time complexity: O(V+E) for most graphs, O(V^2) in the worst case.
int igraph_get_all_shortest_paths_dijkstra(const igraph_t *graph, igraph_vector_ptr_t *res, igraph_vector_t *nrgeo, igraph_integer_t from, igraph_vs_t to, const igraph_vector_t *weights, igraph_neimode_t mode);
Arguments:

The graph object. 


Pointer to an initialized pointer vector, the result
will be stored here in igraph_vector_t objects. Each vector
object contains the vertices along a shortest path from 


Pointer to an initialized igraph_vector_t object or
NULL. If not NULL the number of shortest paths from 


The id of the vertex from/to which the geodesics are calculated. 


Vertex sequence with the ids of the vertices to/from which the shortest paths will be calculated. A vertex might be given multiple times. 


a vector holding the edge weights. All weights must be nonnegative. 


The type of shortest paths to be use for the calculation in directed graphs. Possible values:

Returns:
Error code:

Time complexity: O(ElogE+V), where V is the number of vertices and E is the number of edges
See also:

Example 13.5. File examples/simple/igraph_get_all_shortest_paths_dijkstra.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdlib.h> /* Compares two paths based on their last elements. If they are equal, proceeds * with the ones preceding these elements, until we find a difference. If one * of the vectors is a suffix of the other, the shorter vector gets ordered * first. */ int vector_tail_cmp(const void* path1, const void* path2) { const igraph_vector_t* vec1 = *(const igraph_vector_t**)path1; const igraph_vector_t* vec2 = *(const igraph_vector_t**)path2; size_t length1 = igraph_vector_size(vec1); size_t length2 = igraph_vector_size(vec2); int diff; while (length1 > 0 && length2 > 0) { length1; length2; diff = VECTOR(*vec1)[length1]  VECTOR(*vec2)[length2]; if (diff != 0) { return diff; } } if (length1 == 0 && length2 == 0) { return 0; } else if (length1 == 0) { return 1; } else { return 1; } } void check_nrgeo(igraph_t *graph, igraph_vs_t vs, igraph_vector_ptr_t* paths, igraph_vector_t* nrgeo) { long int i, n; igraph_vector_t nrgeo2, *path; igraph_vit_t vit; n = igraph_vcount(graph); igraph_vector_init(&nrgeo2, n); if (igraph_vector_size(nrgeo) != n) { printf("nrgeo vector length must be %ld, was %ld", n, igraph_vector_size(nrgeo)); return; } n = igraph_vector_ptr_size(paths); for (i = 0; i < n; i++) { path = VECTOR(*paths)[i]; if (path == 0) { printf("Null path found in result vector at index %ld\n", i); return; } if (igraph_vector_size(path) == 0) { printf("Empty path found in result vector at index %ld\n", i); return; } VECTOR(nrgeo2)[(long int)igraph_vector_tail(path)] += 1; } igraph_vit_create(graph, vs, &vit); for (IGRAPH_VIT_RESET(vit); !IGRAPH_VIT_END(vit); IGRAPH_VIT_NEXT(vit)) { long int node = IGRAPH_VIT_GET(vit); if (VECTOR(*nrgeo)[node]  VECTOR(nrgeo2)[node]) { printf("nrgeo[%ld] invalid, observed = %ld, expected = %ld\n", node, (long int)VECTOR(*nrgeo)[node], (long int)VECTOR(nrgeo2)[node]); } } igraph_vit_destroy(&vit); igraph_vector_destroy(&nrgeo2); } int main() { igraph_t g; igraph_vector_ptr_t res; long int i; igraph_real_t weights[] = { 1, 2, 3, 4, 5, 1, 1, 1, 1, 1 }; igraph_real_t weights2[] = { 0, 2, 1, 0, 5, 2, 1, 1, 0, 2, 2, 8, 1, 1, 3, 1, 1, 4, 2, 1 }; igraph_real_t dim[] = { 4, 4 }; igraph_vector_t weights_vec, dim_vec, nrgeo; igraph_vs_t vs; igraph_vector_init(&nrgeo, 0); /* Simple ring graph without weights */ igraph_ring(&g, 10, IGRAPH_UNDIRECTED, 0, 1); igraph_vector_ptr_init(&res, 5); igraph_vs_vector_small(&vs, 1, 3, 4, 5, 2, 1, 1); igraph_get_all_shortest_paths_dijkstra(&g, /*res=*/ &res, /*nrgeo=*/ &nrgeo, /*from=*/ 0, /*to=*/ vs, /*weights=*/ 0, /*mode=*/ IGRAPH_OUT); check_nrgeo(&g, vs, &res, &nrgeo); for (i = 0; i < igraph_vector_ptr_size(&res); i++) { igraph_vector_print(VECTOR(res)[i]); igraph_vector_destroy(VECTOR(res)[i]); free(VECTOR(res)[i]); VECTOR(res)[i] = 0; } /* Same ring, but with weights */ igraph_vector_view(&weights_vec, weights, sizeof(weights) / sizeof(igraph_real_t)); igraph_get_all_shortest_paths_dijkstra(&g, /*res=*/ &res, /*nrgeo=*/ &nrgeo, /*from=*/ 0, /*to=*/ vs, /*weights=*/ &weights_vec, /*mode=*/ IGRAPH_OUT); check_nrgeo(&g, vs, &res, &nrgeo); for (i = 0; i < igraph_vector_ptr_size(&res); i++) { igraph_vector_print(VECTOR(res)[i]); igraph_vector_destroy(VECTOR(res)[i]); free(VECTOR(res)[i]); VECTOR(res)[i] = 0; } igraph_destroy(&g); /* More complicated example */ igraph_small(&g, 10, IGRAPH_DIRECTED, 0, 1, 0, 2, 0, 3, 1, 2, 1, 4, 1, 5, 2, 3, 2, 6, 3, 2, 3, 6, 4, 5, 4, 7, 5, 6, 5, 8, 5, 9, 7, 5, 7, 8, 8, 9, 5, 2, 2, 1, 1); igraph_vector_view(&weights_vec, weights2, sizeof(weights2) / sizeof(igraph_real_t)); igraph_get_all_shortest_paths_dijkstra(&g, /*res=*/ &res, /*nrgeo=*/ &nrgeo, /*from=*/ 0, /*to=*/ vs, /*weights=*/ &weights_vec, /*mode=*/ IGRAPH_OUT); check_nrgeo(&g, vs, &res, &nrgeo); /* Sort the paths in a deterministic manner to avoid problems with * different qsort() implementations on different platforms */ igraph_vector_ptr_sort(&res, vector_tail_cmp); for (i = 0; i < igraph_vector_ptr_size(&res); i++) { igraph_vector_print(VECTOR(res)[i]); igraph_vector_destroy(VECTOR(res)[i]); free(VECTOR(res)[i]); VECTOR(res)[i] = 0; } igraph_vs_destroy(&vs); igraph_destroy(&g); /* Regular lattice with some heavyweight edges */ igraph_vector_view(&dim_vec, dim, sizeof(dim) / sizeof(igraph_real_t)); igraph_lattice(&g, &dim_vec, 1, 0, 0, 0); igraph_vs_vector_small(&vs, 3, 12, 15, 1); igraph_vector_init(&weights_vec, 24); igraph_vector_fill(&weights_vec, 1); VECTOR(weights_vec)[2] = 100; VECTOR(weights_vec)[8] = 100; /* 1>2, 4>8 */ igraph_get_all_shortest_paths_dijkstra(&g, /*res=*/ 0, /*nrgeo=*/ &nrgeo, /*from=*/ 0, /*to=*/ vs, /*weights=*/ &weights_vec, /*mode=*/ IGRAPH_OUT); igraph_vector_destroy(&weights_vec); igraph_vs_destroy(&vs); igraph_destroy(&g); printf("%ld ", (long int)VECTOR(nrgeo)[3]); printf("%ld ", (long int)VECTOR(nrgeo)[12]); printf("%ld\n", (long int)VECTOR(nrgeo)[15]); igraph_vector_ptr_destroy(&res); igraph_vector_destroy(&nrgeo); if (!IGRAPH_FINALLY_STACK_EMPTY) { return 1; } return 0; }
int igraph_get_all_simple_paths(const igraph_t *graph, igraph_vector_int_t *res, igraph_integer_t from, const igraph_vs_t to, igraph_integer_t cutoff, igraph_neimode_t mode);
A path is simple, if its vertices are unique, no vertex is visited more than once.
Note that potentially there are exponentially many paths between two vertices of a graph, and you may run out of memory when using this function, if your graph is latticelike.
This function currently ignored multiple and loop edges.
Arguments:

The input graph. 

Initialized integer vector, all paths are returned here, separated by 1 markers. The paths are included in arbitrary order, as they are found. 

The start vertex. 

The target vertices. 

Maximum length of path that is considered. If negative, paths of all lengths are considered. 

The type of the paths to consider, it is ignored for undirected graphs. 
Returns:
Error code. 
Time complexity: O(n!) in the worst case, n is the number of vertices.
int igraph_average_path_length(const igraph_t *graph, igraph_real_t *res, igraph_bool_t directed, igraph_bool_t unconn);
Arguments:

The graph object. 

Pointer to a real number, this will contain the result. 

Boolean, whether to consider directed paths. Ignored for undirected graphs. 

What to do if the graph is not connected. If

Returns:
Error code:

Time complexity: O(VE), the number of vertices times the number of edges.
Example 13.6. File examples/simple/igraph_average_path_length.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g; igraph_real_t result; igraph_barabasi_game(&g, 30, /*power=*/ 1, 30, 0, 0, /*A=*/ 1, IGRAPH_DIRECTED, IGRAPH_BARABASI_BAG, /*start_from=*/ 0); igraph_average_path_length(&g, &result, IGRAPH_UNDIRECTED, 1); /* printf("Length of the average shortest paths: %f\n", (float) result); */ igraph_destroy(&g); return 0; }
int igraph_path_length_hist(const igraph_t *graph, igraph_vector_t *res, igraph_real_t *unconnected, igraph_bool_t directed);
This function calculates a histogram, by calculating the shortest path length between each pair of vertices. For directed graphs both directions might be considered and then every pair of vertices appears twice in the histogram.
Arguments:

The input graph. 

Pointer to an initialized vector, the result is stored here. The first (i.e. zeroth) element contains the number of shortest paths of length 1, etc. The supplied vector is resized as needed. 

Pointer to a real number, the number of pairs for which the second vertex is not reachable from the first is stored here. 

Whether to consider directed paths in a directed graph (if not zero). This argument is ignored for undirected graphs. 
Returns:
Error code. 
Time complexity: O(VE), the number of vertices times the number of edges.
See also:
int igraph_diameter(const igraph_t *graph, igraph_integer_t *pres, igraph_integer_t *pfrom, igraph_integer_t *pto, igraph_vector_t *path, igraph_bool_t directed, igraph_bool_t unconn);
Arguments:

The graph object. 

Pointer to an integer, if not 

Pointer to an integer, if not 

Pointer to an integer, if not 

Pointer to an initialized vector. If not 

Boolean, whether to consider directed paths. Ignored for undirected graphs. 

What to do if the graph is not connected. If

Returns:
Error code:

Time complexity: O(VE), the number of vertices times the number of edges.
Example 13.7. File examples/simple/igraph_diameter.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_vector(igraph_vector_t *v) { long int i, n = igraph_vector_size(v); for (i = 0; i < n; i++) { printf(" %li", (long int) VECTOR(*v)[i]); } printf("\n"); } int main() { igraph_t g; igraph_integer_t result; igraph_integer_t from, to; igraph_vector_t path; igraph_barabasi_game(&g, 30, /*power=*/ 1, 30, 0, 0, /*A=*/ 1, IGRAPH_DIRECTED, IGRAPH_BARABASI_BAG, /*start_from=*/ 0); igraph_diameter(&g, &result, 0, 0, 0, IGRAPH_UNDIRECTED, 1); /* printf("Diameter: %li\n", (long int) result); */ igraph_destroy(&g); igraph_ring(&g, 10, IGRAPH_DIRECTED, 0, 0); igraph_vector_init(&path, 0); igraph_diameter(&g, &result, &from, &to, &path, IGRAPH_DIRECTED, 1); printf("diameter: %li, from %li to %li\n", (long int) result, (long int) from, (long int) to); print_vector(&path); igraph_vector_destroy(&path); igraph_destroy(&g); return 0; }
int igraph_diameter_dijkstra(const igraph_t *graph, const igraph_vector_t *weights, igraph_real_t *pres, igraph_integer_t *pfrom, igraph_integer_t *pto, igraph_vector_t *path, igraph_bool_t directed, igraph_bool_t unconn);
The diameter of a graph is its longest geodesic. I.e. the (weighted) shortest path is calculated for all pairs of vertices and the longest one is the diameter.
Arguments:

The input graph, can be directed or undirected. 

Pointer to a real number, if not 

Pointer to an integer, if not 

Pointer to an integer, if not 

Pointer to an initialized vector. If not 

Boolean, whether to consider directed paths. Ignored for undirected graphs. 

What to do if the graph is not connected. If

Returns:
Error code. 
Time complexity: O(VE*logE), V is the number of vertices, E is the number of edges.
int igraph_girth(const igraph_t *graph, igraph_integer_t *girth, igraph_vector_t *circle);
The current implementation works for undirected graphs only, directed graphs are treated as undirected graphs. Loop edges and multiple edges are ignored.
If the graph is a forest (ie. acyclic), then zero is returned.
This implementation is based on Alon Itai and Michael Rodeh: Finding a minimum circuit in a graph Proceedings of the ninth annual ACM symposium on Theory of computing , 110, 1977. The first implementation of this function was done by Keith Briggs, thanks Keith.
Arguments:

The input graph. 

Pointer to an integer, if not 

Pointer to an initialized vector, the vertex ids in
the shortest circle will be stored here. If 
Returns:
Error code. 
Time complexity: O((V+E)^2), V is the number of vertices, E is the number of edges in the general case. If the graph has no circles at all then the function needs O(V+E) time to realize this and then it stops.
Example 13.8. File examples/simple/igraph_girth.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20072012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g; igraph_integer_t girth; igraph_vector_t v; igraph_real_t chord[] = { 0, 50 }; igraph_ring(&g, 100, IGRAPH_UNDIRECTED, 0, 1); igraph_vector_view(&v, chord, sizeof(chord) / sizeof(igraph_real_t)); igraph_add_edges(&g, &v, 0); igraph_girth(&g, &girth, 0); if (girth != 51) { return 1; } igraph_destroy(&g); return 0; }
int igraph_eccentricity(const igraph_t *graph, igraph_vector_t *res, igraph_vs_t vids, igraph_neimode_t mode);
The eccentricity of a vertex is calculated by measuring the shortest distance from (or to) the vertex, to (or from) all vertices in the graph, and taking the maximum.
This implementation ignores vertex pairs that are in different components. Isolated vertices have eccentricity zero.
Arguments:

The input graph, it can be directed or undirected. 

Pointer to an initialized vector, the result is stored here. 

The vertices for which the eccentricity is calculated. 

What kind of paths to consider for the calculation:

Returns:
Error code. 
Time complexity: O(v*(V+E)), where V is the number of vertices, E is the number of edges and v is the number of vertices for which eccentricity is calculated.
See also:
Example 13.9. File examples/simple/igraph_eccentricity.c
/* * mode: C * */ /* vim:set ts=4 sts=4 sw=4 et: */ /* IGraph library. Copyright (C) 201112 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g; igraph_vector_t ecc; igraph_vector_init(&ecc, 0); igraph_star(&g, 10, IGRAPH_STAR_UNDIRECTED, 0); igraph_eccentricity(&g, &ecc, igraph_vss_all(), IGRAPH_OUT); igraph_vector_print(&ecc); igraph_destroy(&g); igraph_star(&g, 10, IGRAPH_STAR_OUT, 0); igraph_eccentricity(&g, &ecc, igraph_vss_all(), IGRAPH_ALL); igraph_vector_print(&ecc); igraph_destroy(&g); igraph_star(&g, 10, IGRAPH_STAR_OUT, 0); igraph_eccentricity(&g, &ecc, igraph_vss_all(), IGRAPH_OUT); igraph_vector_print(&ecc); igraph_destroy(&g); igraph_vector_destroy(&ecc); return 0; }
int igraph_radius(const igraph_t *graph, igraph_real_t *radius, igraph_neimode_t mode);
The radius of a graph is the defined as the minimum eccentricity of
its vertices, see igraph_eccentricity()
.
Arguments:

The input graph, it can be directed or undirected. 

Pointer to a real variable, the result is stored here. 

What kind of paths to consider for the calculation:

Returns:
Error code. 
Time complexity: O(V(V+E)), where V is the number of vertices and E is the number of edges.
See also:
Example 13.10. File examples/simple/igraph_radius.c
/* * mode: C * */ /* vim:set ts=4 sts=4 sw=4 et: */ /* IGraph library. Copyright (C) 20112012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g; igraph_real_t radius; igraph_star(&g, 10, IGRAPH_STAR_UNDIRECTED, 0); igraph_radius(&g, &radius, IGRAPH_OUT); if (radius != 1) { return 1; } igraph_destroy(&g); igraph_star(&g, 10, IGRAPH_STAR_OUT, 0); igraph_radius(&g, &radius, IGRAPH_ALL); if (radius != 1) { return 2; } igraph_destroy(&g); igraph_star(&g, 10, IGRAPH_STAR_OUT, 0); igraph_radius(&g, &radius, IGRAPH_OUT); if (radius != 0) { return 3; } igraph_destroy(&g); return 0; }
int igraph_neighborhood_size(const igraph_t *graph, igraph_vector_t *res, igraph_vs_t vids, igraph_integer_t order, igraph_neimode_t mode, igraph_integer_t mindist);
The neighborhood of a given order of a vertex includes all vertices which are closer to the vertex than the order. Ie. order 0 is always the vertex itself, order 1 is the vertex plus its immediate neighbors, order 2 is order 1 plus the immediate neighbors of the vertices in order 1, etc.
This function calculates the size of the neighborhood of the given order for the given vertices.
Arguments:

The input graph. 

Pointer to an initialized vector, the result will be stored here. It will be resized as needed. 

The vertices for which the calculation is performed. 

Integer giving the order of the neighborhood. 

Specifies how to use the direction of the edges if a
directed graph is analyzed. For 

The minimum distance to include a vertex in the counting. If this is one, then the starting vertex is not counted. If this is two, then its neighbors are not counted, either, etc. 
Returns:
Error code. 
See also:

Time complexity: O(n*d*o), where n is the number vertices for which the calculation is performed, d is the average degree, o is the order.
int igraph_neighborhood(const igraph_t *graph, igraph_vector_ptr_t *res, igraph_vs_t vids, igraph_integer_t order, igraph_neimode_t mode, igraph_integer_t mindist);
The neighborhood of a given order of a vertex includes all vertices which are closer to the vertex than the order. Ie. order 0 is always the vertex itself, order 1 is the vertex plus its immediate neighbors, order 2 is order 1 plus the immediate neighbors of the vertices in order 1, etc.
This function calculates the vertices within the neighborhood of the specified vertices.
Arguments:

The input graph. 

An initialized pointer vector. Note that the objects
(pointers) in the vector will not be freed, but the pointer
vector will be resized as needed. The result of the calculation
will be stored here in 

The vertices for which the calculation is performed. 

Integer giving the order of the neighborhood. 

Specifies how to use the direction of the edges if a
directed graph is analyzed. For 

The minimum distance to include a vertex in the counting. If this is one, then the starting vertex is not counted. If this is two, then its neighbors are not counted, either, etc. 
Returns:
Error code. 
See also:

Time complexity: O(n*d*o), n is the number of vertices for which the calculation is performed, d is the average degree, o is the order.
int igraph_neighborhood_graphs(const igraph_t *graph, igraph_vector_ptr_t *res, igraph_vs_t vids, igraph_integer_t order, igraph_neimode_t mode, igraph_integer_t mindist);
The neighborhood of a given order of a vertex includes all vertices which are closer to the vertex than the order. Ie. order 0 is always the vertex itself, order 1 is the vertex plus its immediate neighbors, order 2 is order 1 plus the immediate neighbors of the vertices in order 1, etc.
This function finds every vertex in the neighborhood of a given parameter vertex and creates a graph from these vertices.
The first version of this function was written by Vincent Matossian, thanks Vincent.
Arguments:

The input graph. 

Pointer to a pointer vector, the result will be stored
here, ie. 

The vertices for which the calculation is performed. 

Integer giving the order of the neighborhood. 

Specifies how to use the direction of the edges if a
directed graph is analyzed. For 

The minimum distance to include a vertex in the counting. If this is one, then the starting vertex is not counted. If this is two, then its neighbors are not counted, either, etc. 
Returns:
Error code. 
See also:

Time complexity: O(n*(V+E)), where n is the number vertices for which the calculation is performed, V and E are the number of vertices and edges in the original input graph.
The scan statistic is a summary of the locality statistics that is computed from the local neighborhood of each vertex. For details, see Priebe, C. E., Conroy, J. M., Marchette, D. J., Park, Y. (2005). Scan Statistics on Enron Graphs. Computational and Mathematical Organization Theory.
int igraph_local_scan_0(const igraph_t *graph, igraph_vector_t *res, const igraph_vector_t *weights, igraph_neimode_t mode);
K=0 scanstatistics is arbitrarily defined as the vertex degree for
unweighted, and the vertex strength for weighted graphs. See igraph_degree()
and igraph_strength()
.
Arguments:

The input graph 

An initialized vector, the results are stored here. 

Weight vector for weighted graphs, null pointer for unweighted graphs. 

Type of the neighborhood, 
Returns:
Error code. 
int igraph_local_scan_1_ecount(const igraph_t *graph, igraph_vector_t *res, const igraph_vector_t *weights, igraph_neimode_t mode);
Count the number of edges or the sum the edge weights in the 1neighborhood of vertices.
Arguments:

The input graph 

An initialized vector, the results are stored here. 

Weight vector for weighted graphs, null pointer for unweighted graphs. 

Type of the neighborhood, 
Returns:
Error code. 
int igraph_local_scan_k_ecount(const igraph_t *graph, int k, igraph_vector_t *res, const igraph_vector_t *weights, igraph_neimode_t mode);
Count the number of edges or the sum the edge weights in the kneighborhood of vertices.
Arguments:

The input graph 

The size of the neighborhood, nonnegative integer.
The k=0 case is special, see 

An initialized vector, the results are stored here. 

Weight vector for weighted graphs, null pointer for unweighted graphs. 

Type of the neighborhood, 
Returns:
Error code. 
int igraph_local_scan_0_them(const igraph_t *us, const igraph_t *them, igraph_vector_t *res, const igraph_vector_t *weights_them, igraph_neimode_t mode);
K=0 scanstatistics is arbitrarily defined as the vertex degree for
unweighted, and the vertex strength for weighted graphs. See igraph_degree()
and igraph_strength()
.
Arguments:

The input graph, to use to extract the neighborhoods. 

The input graph to use for the actually counting. 

An initialized vector, the results are stored here. 

Weight vector for weighted graphs, null pointer for unweighted graphs. 

Type of the neighborhood, 
Returns:
Error code. 
int igraph_local_scan_1_ecount_them(const igraph_t *us, const igraph_t *them, igraph_vector_t *res, const igraph_vector_t *weights_them, igraph_neimode_t mode);
Count the number of edges or the sum the edge weights in the 1neighborhood of vertices.
Arguments:

The input graph to extract the neighborhoods. 

The input graph to perform the counting. 

Weight vector for weighted graphs, null pointer for unweighted graphs. 

Type of the neighborhood, 
Returns:
Error code. 
See also:

int igraph_local_scan_k_ecount_them(const igraph_t *us, const igraph_t *them, int k, igraph_vector_t *res, const igraph_vector_t *weights_them, igraph_neimode_t mode);
Count the number of edges or the sum the edge weights in the kneighborhood of vertices.
Arguments:

The input graph to extract the neighborhoods. 

The input graph to perform the counting. 

The size of the neighborhood, nonnegative integer.
The k=0 case is special, see 

Weight vector for weighted graphs, null pointer for unweighted graphs. 

Type of the neighborhood, 
Returns:
Error code. 
See also:

int igraph_local_scan_neighborhood_ecount(const igraph_t *graph, igraph_vector_t *res, const igraph_vector_t *weights, const igraph_vector_ptr_t *neighborhoods);
Count the number of edges, or sum the edge weigths in neighborhoods given as a parameter.
Arguments:

The graph to perform the counting/summing in. 

Initialized vector, the result is stored here. 

Weight vector for weighted graphs, null pointer for unweighted graphs. 

List of 
Returns:
Error code. 
igraph_subcomponent
— The vertices in the same component as a given vertex.igraph_induced_subgraph
— Creates a subgraph induced by the specified vertices.igraph_subgraph_edges
— Creates a subgraph with the specified edges and their endpoints.igraph_subgraph
— Creates a subgraph induced by the specified vertices.igraph_clusters
— Calculates the (weakly or strongly) connected components in a graph.igraph_is_connected
— Decides whether the graph is (weakly or strongly) connected.igraph_decompose
— Decompose a graph into connected components.igraph_decompose_destroy
— Free the memory allocated by igraph_decompose()
.igraph_biconnected_components
— Calculate biconnected componentsigraph_articulation_points
— Find the articulation points in a graph.igraph_bridges
— Find all bridges in a graph.
int igraph_subcomponent(const igraph_t *graph, igraph_vector_t *res, igraph_real_t vertex, igraph_neimode_t mode);
Arguments:

The graph object. 


The result, vector with the ids of the vertices in the same component. 


The id of the vertex of which the component is searched. 


Type of the component for directed graphs, possible values:

Returns:
Error code:

Time complexity: O(V+E), V and E are the number of vertices and edges in the graph.
See also:

int igraph_induced_subgraph(const igraph_t *graph, igraph_t *res, const igraph_vs_t vids, igraph_subgraph_implementation_t impl);
This function collects the specified vertices and all edges between them to a new graph. As the vertex ids in a graph always start with zero, this function very likely needs to reassign ids to the vertices.
Arguments:

The graph object. 

The subgraph, another graph object will be stored here,
do not initialize this object before calling this
function, and call 

A vertex selector describing which vertices to keep. 

This parameter selects which implementation should we
use when constructing the new graph. Basically there are two
possibilities: 
Returns:
Error code:

Time complexity: O(V+E), V and E are the number of vertices and edges in the original graph.
See also:

int igraph_subgraph_edges(const igraph_t *graph, igraph_t *res, const igraph_es_t eids, igraph_bool_t delete_vertices);
This function collects the specified edges and their endpoints to a new graph. As the vertex ids in a graph always start with zero, this function very likely needs to reassign ids to the vertices.
Arguments:

The graph object. 

The subgraph, another graph object will be stored here,
do not initialize this object before calling this
function, and call 

An edge selector describing which edges to keep. 

Whether to delete the vertices not incident on any
of the specified edges as well. If 
Returns:
Error code:

Time complexity: O(V+E), V and E are the number of vertices and edges in the original graph.
See also:

int igraph_subgraph(const igraph_t *graph, igraph_t *res, const igraph_vs_t vids);
This function is an alias to igraph_induced_subgraph()
, it is
left here to ensure API compatibility with igraph versions prior to 0.6.
This function collects the specified vertices and all edges between them to a new graph. As the vertex ids in a graph always start with zero, this function very likely needs to reassign ids to the vertices.
Arguments:

The graph object. 

The subgraph, another graph object will be stored here,
do not initialize this object before calling this
function, and call 

A vertex selector describing which vertices to keep. 
Returns:
Error code:

Time complexity: O(V+E), V and E are the number of vertices and edges in the original graph.
See also:

int igraph_clusters(const igraph_t *graph, igraph_vector_t *membership, igraph_vector_t *csize, igraph_integer_t *no, igraph_connectedness_t mode);
Arguments:

The graph object to analyze. 

First half of the result will be stored here. For
every vertex the id of its component is given. The vector
has to be preinitialized and will be resized. Alternatively
this argument can be 

The second half of the result. For every component it
gives its size, the order is defined by the component ids.
The vector has to be preinitialized and will be resized.
Alternatively this argument can be 

Pointer to an integer, if not 

For directed graph this specifies whether to calculate
weakly or strongly connected components. Possible values:

Returns:
Error code:

Time complexity: O(V+E), V and E are the number of vertices and edges in the graph.
int igraph_is_connected(const igraph_t *graph, igraph_bool_t *res, igraph_connectedness_t mode);
A graph with zero vertices (i.e. the null graph) is connected by definition.
Arguments:

The graph object to analyze. 

Pointer to a logical variable, the result will be stored here. 

For a directed graph this specifies whether to calculate
weak or strong connectedness. Possible values:

Returns:
Error code:

Time complexity: O(V+E), the number of vertices plus the number of edges in the graph.
int igraph_decompose(const igraph_t *graph, igraph_vector_ptr_t *components, igraph_connectedness_t mode, long int maxcompno, long int minelements);
Create separate graph for each component of a graph. Note that the vertex ids in the new graphs will be different than in the original graph. (Except if there is only one component in the original graph.)
Arguments:

The original graph. 

This pointer vector will contain pointers to the
subcomponent graphs. It should be initialized before calling this
function and will be resized to hold the graphs. Don't forget to
call 

Either 

The maximum number of components to return. The
first 

The minimum number of vertices a component
should contain in order to place it in the 
Returns:
Error code, 
Added in version 0.2.
Time complexity: O(V+E), the number of vertices plus the number of edges.
Example 13.11. File examples/simple/igraph_decompose.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdlib.h> void free_complist(igraph_vector_ptr_t *complist) { long int i; for (i = 0; i < igraph_vector_ptr_size(complist); i++) { igraph_destroy(VECTOR(*complist)[i]); free(VECTOR(*complist)[i]); } } int main() { igraph_t ring, g; igraph_vector_ptr_t complist; long int i; igraph_real_t edges[] = { 0, 1, 1, 2, 2, 0, 3, 4, 4, 5, 5, 6, 8, 9, 9, 10 }; igraph_vector_t v; /* A ring, a single component */ igraph_ring(&ring, 10, IGRAPH_UNDIRECTED, 0, 1); igraph_vector_ptr_init(&complist, 0); igraph_decompose(&ring, &complist, IGRAPH_WEAK, 1, 0); igraph_write_graph_edgelist(VECTOR(complist)[0], stdout); free_complist(&complist); igraph_destroy(&ring); /* random graph with a giant component */ igraph_erdos_renyi_game(&g, IGRAPH_ERDOS_RENYI_GNP, 100, 4.0 / 100, IGRAPH_UNDIRECTED, 0); igraph_decompose(&g, &complist, IGRAPH_WEAK, 1, 20); if (igraph_vector_ptr_size(&complist) != 1) { return 1; } free_complist(&complist); igraph_destroy(&g); /* a toy graph, three components maximum, with at least 2 vertices each */ igraph_create(&g, igraph_vector_view(&v, edges, sizeof(edges) / sizeof(igraph_real_t)), 0, IGRAPH_DIRECTED); igraph_decompose(&g, &complist, IGRAPH_WEAK, 3, 2); for (i = 0; i < igraph_vector_ptr_size(&complist); i++) { igraph_write_graph_edgelist(VECTOR(complist)[i], stdout); } free_complist(&complist); igraph_destroy(&g); /* The same graph, this time with vertex attributes */ /* igraph_vector_init_seq(&idvect, 0, igraph_vcount(&g)1); */ /* igraph_add_vertex_attribute(&g, "id", IGRAPH_ATTRIBUTE_NUM); */ /* igraph_set_vertex_attributes(&g, "id", IGRAPH_VS_ALL(&g), &idvect); */ /* igraph_vector_destroy(&idvect); */ /* igraph_decompose(&g, &complist, IGRAPH_WEAK, 3, 2); */ /* for (i=0; i<igraph_vector_ptr_size(&complist); i++) { */ /* igraph_t *comp=VECTOR(complist)[i]; */ /* igraph_es_t es; */ /* igraph_es_all(comp, &es); */ /* while (!igraph_es_end(comp, &es)) { */ /* igraph_real_t *from, *to; */ /* igraph_get_vertex_attribute(comp, "id", igraph_es_from(comp, &es), */ /* (void**) &from, 0); */ /* igraph_get_vertex_attribute(comp, "id", igraph_es_to(comp, &es), */ /* (void**) &to, 0); */ /* printf("%li %li\n", (long int) *from, (long int) *to); */ /* igraph_es_next(comp, &es); */ /* } */ /* } */ /* free_complist(&complist); */ /* igraph_destroy(&g); */ igraph_vector_ptr_destroy(&complist); return 0; }
igraph_decompose_destroy
— Free the memory allocated by igraph_decompose()
.
void igraph_decompose_destroy(igraph_vector_ptr_t *complist);
Arguments:

The list of graph components, as returned by

Time complexity: O(c), c is the number of components.
int igraph_biconnected_components(const igraph_t *graph, igraph_integer_t *no, igraph_vector_ptr_t *tree_edges, igraph_vector_ptr_t *component_edges, igraph_vector_ptr_t *components, igraph_vector_t *articulation_points);
A graph is biconnected if the removal of any single vertex (and its incident edges) does not disconnect it.
A biconnected component of a graph is a maximal biconnected subgraph of it. The biconnected components of a graph can be given by the partition of its edges: every edge is a member of exactly one biconnected component. Note that this is not true for vertices: the same vertex can be part of many biconnected components.
Somewhat arbitrarily, igraph does not consider comppnents containing a single vertex only as being biconnected. Isolated vertices will not be part of any of the biconnected components.
Arguments:

The input graph. 

The number of biconnected components will be stored here. 

If not a NULL pointer, then the found components
are stored here, in a list of vectors. Every vector in the list
is a biconnected component, represented by its edges. More precisely,
a spanning tree of the biconnected component is returned.
Note you'll have to
destroy each vector first by calling 

If not a NULL pointer, then the edges of the
biconnected components are stored here, in the same form as for


If not a NULL pointer, then the vertices of the biconnected components are stored here, in the same format as for the previous two arguments. 

If not a NULL pointer, then the articulation points of the graph are stored in this vector. A vertex is an articulation point if its removal increases the number of (weakly) connected components in the graph. 
Returns:
Error code. 
Time complexity: O(V+E), linear in the number of vertices and
edges, but only if you do not calculate components
and
component_edges
. If you calculate components
, then it is
quadratic in the number of vertices. If you calculate component_edges
as well, then it is cubic in the number of
vertices.
See also:
Example 13.12. File examples/simple/igraph_biconnected_components.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdlib.h> void sort_and_print_vector(igraph_vector_t *v) { long int i, n = igraph_vector_size(v); igraph_vector_sort(v); for (i = 0; i < n; i++) { printf(" %li", (long int) VECTOR(*v)[i]); } printf("\n"); } void warning_handler_ignore(const char* reason, const char* file, int line, int e) { } int main() { igraph_t g; igraph_vector_ptr_t result; igraph_integer_t no; long int i; igraph_set_warning_handler(warning_handler_ignore); igraph_vector_ptr_init(&result, 0); igraph_small(&g, 7, 0, 0, 1, 1, 2, 2, 3, 3, 0, 2, 4, 4, 5, 2, 5, 1); igraph_biconnected_components(&g, &no, 0, 0, &result, 0); if (no != 2  no != igraph_vector_ptr_size(&result)) { return 1; } for (i = 0; i < no; i++) { sort_and_print_vector((igraph_vector_t*)VECTOR(result)[i]); igraph_vector_destroy((igraph_vector_t*)VECTOR(result)[i]); free((igraph_vector_t*)VECTOR(result)[i]); } igraph_biconnected_components(&g, &no, 0, &result, 0, 0); if (no != 2  no != igraph_vector_ptr_size(&result)) { return 2; } for (i = 0; i < no; i++) { sort_and_print_vector((igraph_vector_t*)VECTOR(result)[i]); igraph_vector_destroy((igraph_vector_t*)VECTOR(result)[i]); free((igraph_vector_t*)VECTOR(result)[i]); } igraph_biconnected_components(&g, &no, &result, 0, 0, 0); if (no != 2  no != igraph_vector_ptr_size(&result)) { return 3; } for (i = 0; i < no; i++) { sort_and_print_vector((igraph_vector_t*)VECTOR(result)[i]); igraph_vector_destroy((igraph_vector_t*)VECTOR(result)[i]); free((igraph_vector_t*)VECTOR(result)[i]); } igraph_vector_ptr_destroy(&result); igraph_destroy(&g); return 0; }
int igraph_articulation_points(const igraph_t *graph, igraph_vector_t *res);
A vertex is an articulation point if its removal increases the number of connected components in the graph.
Arguments:

The input graph. 

Pointer to an initialized vector, the articulation points will be stored here. 
Returns:
Error code. 
Time complexity: O(V+E), linear in the number of vertices and edges.
See also:
int igraph_bridges(const igraph_t *graph, igraph_vector_t *bridges);
An edge is a bridge if its removal increases the number of (weakly) connected components in the graph.
Arguments:

The input graph. 

Pointer to an initialized vector, the bridges will be stored here as edge indices. 
Returns:
Error code. 
Time complexity: O(V+E), linear in the number of vertices and edges.
See also:
int igraph_is_degree_sequence(const igraph_vector_t *out_degrees, const igraph_vector_t *in_degrees, igraph_bool_t *res);
A sequence of n integers is a valid degree sequence if there exists some
graph where the degree of the ith vertex is equal to the ith element of the
sequence. Note that the graph may contain multiple or loop edges; if you are
interested in whether the degrees of some simple graph may realize the
given sequence, use igraph_is_graphical_degree_sequence
.
In particular, the function checks whether all the degrees are nonnegative. For undirected graphs, it also checks whether the sum of degrees is even. For directed graphs, the function checks whether the lengths of the two degree vectors are equal and whether their sums are also equal. These are known sufficient and necessary conditions for a degree sequence to be valid.
Arguments:

an integer vector specifying the degree sequence for undirected graphs or the outdegree sequence for directed graphs. 

an integer vector specifying the indegrees of the vertices for directed graphs. For undirected graphs, this must be null. 

pointer to a boolean variable, the result will be stored here 
Returns:
Error code. 
Time complexity: O(n), where n is the length of the degree sequence.
int igraph_is_graphical_degree_sequence(const igraph_vector_t *out_degrees, const igraph_vector_t *in_degrees, igraph_bool_t *res);
simple graph.
References:
Hakimi SL: On the realizability of a set of integers as degrees of the vertices of a simple graph. J SIAM Appl Math 10:496506, 1962.
PL Erdos, I Miklos and Z Toroczkai: A simple HavelHakimi type algorithm to realize graphical degree sequences of directed graphs. The Electronic Journal of Combinatorics 17(1):R66, 2010.
Z Kiraly: Recognizing graphic degree sequences and generating all realizations. TR201111, Egervary Research Group, H1117, Budapest, Hungary. ISSN 15874451, 2012.
Arguments:

an integer vector specifying the degree sequence for undirected graphs or the outdegree sequence for directed graphs. 

an integer vector specifying the indegrees of the vertices for directed graphs. For undirected graphs, this must be null. 

pointer to a boolean variable, the result will be stored here 
Returns:
Error code. 
Time complexity: O(n log n) for undirected graphs, O(n^2) for directed graphs, where n is the length of the degree sequence.
igraph_closeness
— Closeness centrality calculations for some vertices.igraph_betweenness
— Betweenness centrality of some vertices.igraph_edge_betweenness
— Betweenness centrality of the edges.igraph_pagerank_algo_t
— PageRank algorithm implementationigraph_pagerank_power_options_t
— Options for the power methodigraph_pagerank
— Calculates the Google PageRank for the specified vertices.igraph_pagerank_old
— Calculates the Google PageRank for the specified vertices.igraph_personalized_pagerank
— Calculates the personalized Google PageRank for the specified vertices.igraph_personalized_pagerank_vs
— Calculates the personalized Google PageRank for the specified vertices.igraph_constraint
— Burt's constraint scores.igraph_maxdegree
— Calculate the maximum degree in a graph (or set of vertices).igraph_strength
— Strength of the vertices, weighted vertex degree in other words.igraph_eigenvector_centrality
— Eigenvector centrality of the verticesigraph_hub_score
— Kleinberg's hub scoresigraph_authority_score
— Kleinerg's authority scores
int igraph_closeness(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, igraph_neimode_t mode, const igraph_vector_t *weights, igraph_bool_t normalized);
The closeness centrality of a vertex measures how easily other vertices can be reached from it (or the other way: how easily it can be reached from the other vertices). It is defined as the number of vertices minus one divided by the sum of the lengths of all geodesics from/to the given vertex.
If the graph is not connected, and there is no path between two vertices, the number of vertices is used instead the length of the geodesic. This is longer than the longest possible geodesic in case of unweighted graphs, but may not be so in weighted graphs, so it is best not to use this function on weighted graphs.
If the graph has a single vertex only, the closeness centrality of that single vertex will be NaN (because we are essentially dividing zero with zero).
Arguments:

The graph object. 


The result of the computation, a vector containing the closeness centrality scores for the given vertices. 


The vertices for which the closeness centrality will be computed. 


The type of shortest paths to be used for the calculation in directed graphs. Possible values:



An optional vector containing edge weights for weighted closeness. Supply a null pointer here for traditional, unweighted closeness. 


Boolean, whether to normalize results by multiplying by the number of vertices minus one. 
Returns:
Error code:

Time complexity: O(nE), n is the number of vertices for which the calculation is done and E is the number of edges in the graph.
See also:
Other centrality types: 
int igraph_betweenness(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, igraph_bool_t directed, const igraph_vector_t* weights, igraph_bool_t nobigint);
The betweenness centrality of a vertex is the number of geodesics going through it. If there are more than one geodesic between two vertices, the value of these geodesics are weighted by one over the number of geodesics.
Arguments:

The graph object. 

The result of the computation, a vector containing the betweenness scores for the specified vertices. 

The vertices of which the betweenness centrality scores will be calculated. 

Logical, if true directed paths will be considered for directed graphs. It is ignored for undirected graphs. 

An optional vector containing edge weights for calculating weighted betweenness. Supply a null pointer here for unweighted betweenness. 

Logical, if true, then we don't use big integers for the calculation, setting this to 1 (=true) should work for most graphs. It is currently ignored for weighted graphs. 
Returns:
Error code:

Time complexity: O(VE), V and E are the number of vertices and edges in the graph. Note that the time complexity is independent of the number of vertices for which the score is calculated.
See also:
Other centrality types: 
Example 13.13. File examples/simple/igraph_betweenness.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20082012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_vector(igraph_vector_t *v, FILE *f) { long int i; for (i = 0; i < igraph_vector_size(v); i++) { fprintf(f, " %li", (long int) VECTOR(*v)[i]); } fprintf(f, "\n"); } int main() { igraph_t g; igraph_vector_t bet, bet2, weights, edges; igraph_real_t nontriv[] = { 0, 19, 0, 16, 0, 20, 1, 19, 2, 5, 3, 7, 3, 8, 4, 15, 4, 11, 5, 8, 5, 19, 6, 7, 6, 10, 6, 8, 6, 9, 7, 20, 9, 10, 9, 20, 10, 19, 11, 12, 11, 20, 12, 15, 13, 15, 14, 18, 14, 16, 14, 17, 15, 16, 17, 18 }; igraph_real_t nontriv_weights[] = { 0.5249, 1, 0.1934, 0.6274, 0.5249, 0.0029, 0.3831, 0.05, 0.6274, 0.3831, 0.5249, 0.0587, 0.0579, 0.0562, 0.0562, 0.1934, 0.6274, 0.6274, 0.6274, 0.0418, 0.6274, 0.3511, 0.3511, 0.1486, 1, 1, 0.0711, 0.2409 }; igraph_real_t nontriv_res[] = { 20, 0, 0, 0, 0, 19, 80, 85, 32, 0, 10, 75, 70, 0, 36, 81, 60, 0, 19, 19, 86 }; /*******************************************************/ igraph_barabasi_game(/* graph= */ &g, /* n= */ 1000, /* power= */ 1, /* m= */ 3, /* outseq= */ 0, /* outpref= */ 0, /* A= */ 1, /* directed= */ 0, /* algo= */ IGRAPH_BARABASI_BAG, /* start_from= */ 0); igraph_simplify(&g, /* multiple= */ 1, /* loops= */ 1, /*edge_comb=*/ 0); igraph_vector_init(&bet, 0); igraph_betweenness_estimate(/* graph= */ &g, /* res= */ &bet, /* vids= */ igraph_vss_all(), /* directed = */ 0, /* cutoff= */ 2, /* weights= */ 0, /* nobigint= */ 1); igraph_vector_destroy(&bet); igraph_destroy(&g); /*******************************************************/ igraph_tree(&g, 20000, 10, IGRAPH_TREE_UNDIRECTED); igraph_vector_init(&bet, 0); igraph_betweenness_estimate(/* graph= */ &g, /* res= */ &bet, /* vids= */ igraph_vss_all(), /* directed = */ 0, /* cutoff= */ 3, /* weights= */ 0, /* nobigint= */ 1); igraph_vector_init(&bet2, 0); igraph_vector_init(&weights, igraph_ecount(&g)); igraph_vector_fill(&weights, 1.0); igraph_betweenness_estimate(/* graph= */ &g, /* res= */ &bet2, /* vids= */ igraph_vss_all(), /* directed = */ 0, /* cutoff= */ 3, /* weights= */ &weights, /* nobigint= */ 1); if (!igraph_vector_all_e(&bet, &bet2)) { return 1; } igraph_vector_destroy(&bet); igraph_vector_destroy(&bet2); igraph_vector_destroy(&weights); igraph_destroy(&g); /* Nontrivial weighted graph */ igraph_vector_view(&edges, nontriv, sizeof(nontriv) / sizeof(igraph_real_t)); igraph_create(&g, &edges, 0, /* directed= */ 0); igraph_vector_view(&weights, nontriv_weights, sizeof(nontriv_weights) / sizeof(igraph_real_t)); igraph_vector_init(&bet, 0); igraph_betweenness(/*graph=*/ &g, /*res=*/ &bet, /*vids=*/ igraph_vss_all(), /*directed=*/0, /*weights=*/ &weights, /*nobigint=*/ 1); igraph_vector_view(&bet2, nontriv_res, sizeof(nontriv_res) / sizeof(igraph_real_t)); if (!igraph_vector_all_e(&bet, &bet2)) { return 2; } igraph_vector_destroy(&bet); igraph_destroy(&g); /* test corner case of cutoff = 0 */ igraph_tree(&g, 20, 3, IGRAPH_TREE_UNDIRECTED); /* unweighted */ igraph_vector_init(&bet, 0); igraph_betweenness_estimate(/* graph= */ &g, /* res= */ &bet, /* vids= */ igraph_vss_all(), /* directed = */ 0, /* cutoff= */ 0, /* weights= */ 0, /* nobigint= */ 1); igraph_vector_init(&bet2, 0); igraph_betweenness_estimate(/* graph= */ &g, /* res= */ &bet2, /* vids= */ igraph_vss_all(), /* directed = */ 0, /* cutoff= */ 1, /* weights= */ 0, /* nobigint= */ 1); if (!igraph_vector_all_e(&bet, &bet2)) { return 1; } igraph_vector_destroy(&bet); igraph_vector_destroy(&bet2); /* weighted */ igraph_vector_init(&weights, igraph_ecount(&g)); igraph_vector_fill(&weights, 2.0); igraph_vector_init(&bet, 0); igraph_betweenness_estimate(/* graph= */ &g, /* res= */ &bet, /* vids= */ igraph_vss_all(), /* directed = */ 0, /* cutoff= */ 0, /* weights= */ &weights, /* nobigint= */ 1); igraph_vector_init(&bet2, 0); igraph_betweenness_estimate(/* graph= */ &g, /* res= */ &bet2, /* vids= */ igraph_vss_all(), /* directed = */ 0, /* cutoff= */ 1, /* weights= */ &weights, /* nobigint= */ 1); if (!igraph_vector_all_e(&bet, &bet2)) { return 1; } igraph_vector_destroy(&bet); igraph_vector_destroy(&bet2); igraph_vector_destroy(&weights); igraph_destroy(&g); return 0; }
int igraph_edge_betweenness(const igraph_t *graph, igraph_vector_t *result, igraph_bool_t directed, const igraph_vector_t *weights);
The betweenness centrality of an edge is the number of geodesics going through it. If there are more than one geodesics between two vertices, the value of these geodesics are weighted by one over the number of geodesics.
Arguments:

The graph object. 

The result of the computation, vector containing the betweenness scores for the edges. 

Logical, if true directed paths will be considered for directed graphs. It is ignored for undirected graphs. 

An optional weight vector for weighted edge betweenness. Supply a null pointer here for the unweighted version. 
Returns:
Error code:

Time complexity: O(VE), V and E are the number of vertices and edges in the graph.
See also:
Other centrality types: 
Example 13.14. File examples/simple/igraph_edge_betweenness.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_vector(igraph_vector_t *v, FILE *f) { long int i; for (i = 0; i < igraph_vector_size(v); i++) { fprintf(f, "%.5f\n", (double) VECTOR(*v)[i]); } fprintf(f, "\n"); } int test_bug950() { /* Testing the case of weighted graphs with multiple alternate * paths to the same node with slightly different weights due to * floating point inaccuracies. */ igraph_t g; igraph_vector_t eb; igraph_vector_t weights; igraph_integer_t from, to; long int i; igraph_full(&g, 6, 0, 0); igraph_vector_init(&weights, igraph_ecount(&g)); igraph_vector_init(&eb, igraph_ecount(&g)); for (i = 0; i < igraph_ecount(&g); i++) { igraph_edge(&g, i, &from, &to); VECTOR(weights)[i] = ((from < 3 && to < 3)  (from >= 3 && to >= 3)) ? 1 : 0.1; } igraph_edge_betweenness(&g, &eb, IGRAPH_UNDIRECTED, &weights); print_vector(&eb, stdout); igraph_vector_destroy(&eb); igraph_vector_destroy(&weights); igraph_destroy(&g); return 0; } int test_bug1050() { /* compare cutoff = 1 with cutoff = 0 */ igraph_t g; igraph_vector_t eb, eb2; igraph_full(&g, 6, 0, 0); /* unweighted */ igraph_vector_init(&eb, igraph_ecount(&g)); igraph_vector_init(&eb2, igraph_ecount(&g)); igraph_edge_betweenness_estimate(&g, &eb, IGRAPH_UNDIRECTED, /* cutoff */ 1, /* weights */ 0); igraph_edge_betweenness_estimate(&g, &eb2, IGRAPH_UNDIRECTED, /* cutoff */ 0, /* weights */ 0); if (!igraph_vector_all_e(&eb, &eb2)) { return 1; } igraph_vector_destroy(&eb); igraph_vector_destroy(&eb2); /* weighted */ igraph_vector_t weights; igraph_vector_init(&eb, igraph_ecount(&g)); igraph_vector_init(&eb2, igraph_ecount(&g)); igraph_vector_init(&weights, igraph_ecount(&g)); igraph_vector_fill(&weights, 1); VECTOR(weights)[0] = 2; igraph_edge_betweenness_estimate(&g, &eb, IGRAPH_UNDIRECTED, /* cutoff */ 1, &weights); igraph_edge_betweenness_estimate(&g, &eb2, IGRAPH_UNDIRECTED, /* cutoff */ 0, &weights); if (!igraph_vector_all_e(&eb, &eb2)) { return 1; } igraph_vector_destroy(&eb); igraph_vector_destroy(&eb2); igraph_vector_destroy(&weights); igraph_destroy(&g); return 0; } int main() { igraph_t g; igraph_vector_t eb; igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 10, 0, 11, 0, 12, 0, 13, 0, 17, 0, 19, 0, 21, 0, 31, 1, 2, 1, 3, 1, 7, 1, 13, 1, 17, 1, 19, 1, 21, 1, 30, 2, 3, 2, 7, 2, 8, 2, 9, 2, 13, 2, 27, 2, 28, 2, 32, 3, 7, 3, 12, 3, 13, 4, 6, 4, 10, 5, 6, 5, 10, 5, 16, 6, 16, 8, 30, 8, 32, 8, 33, 9, 33, 13, 33, 14, 32, 14, 33, 15, 32, 15, 33, 18, 32, 18, 33, 19, 33, 20, 32, 20, 33, 22, 32, 22, 33, 23, 25, 23, 27, 23, 29, 23, 32, 23, 33, 24, 25, 24, 27, 24, 31, 25, 31, 26, 29, 26, 33, 27, 33, 28, 31, 28, 33, 29, 32, 29, 33, 30, 32, 30, 33, 31, 32, 31, 33, 32, 33, 1); igraph_vector_init(&eb, igraph_ecount(&g)); igraph_edge_betweenness(&g, &eb, IGRAPH_UNDIRECTED, /*weights=*/ 0); print_vector(&eb, stdout); igraph_vector_destroy(&eb); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 1, 0, 2, 0, 3, 1, 4, 1); igraph_vector_init(&eb, igraph_ecount(&g)); igraph_edge_betweenness_estimate(&g, &eb, IGRAPH_UNDIRECTED, 2, /*weights=*/ 0); print_vector(&eb, stdout); igraph_vector_destroy(&eb); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 1, 0, 3, 1, 2, 1, 4, 2, 5, 3, 4, 3, 6, 4, 5, 4, 7, 5, 8, 6, 7, 7, 8, 1); igraph_vector_init(&eb, igraph_ecount(&g)); igraph_edge_betweenness_estimate(&g, &eb, IGRAPH_UNDIRECTED, 2, /*weights=*/ 0); print_vector(&eb, stdout); igraph_vector_destroy(&eb); igraph_destroy(&g); if (test_bug950()) { return 1; } if (test_bug1050()) { return 2; } return 0; }
typedef enum { IGRAPH_PAGERANK_ALGO_POWER = 0, IGRAPH_PAGERANK_ALGO_ARPACK = 1, IGRAPH_PAGERANK_ALGO_PRPACK = 2 } igraph_pagerank_algo_t;
Algorithms to calculate PageRank.
Values:

Use a simple power iteration, as it was implemented before igraph version 0.5. 

Use the ARPACK library, this was the PageRank implementation in igraph from version 0.5, until version 0.7. 

Use the PRPACK library. Currently this implementation is recommended. 
typedef struct igraph_pagerank_power_options_t { igraph_integer_t niter; igraph_real_t eps; } igraph_pagerank_power_options_t;
Values:

The number of iterations to perform, integer. 

The algorithm will consider the calculation as complete if the difference of values between iterations change less than this value for every vertex. 
int igraph_pagerank(const igraph_t *graph, igraph_pagerank_algo_t algo, igraph_vector_t *vector, igraph_real_t *value, const igraph_vs_t vids, igraph_bool_t directed, igraph_real_t damping, const igraph_vector_t *weights, void *options);
Starting from version 0.7, igraph has three PageRank implementations,
and the user can choose between them. The first implementation is
IGRAPH_PAGERANK_ALGO_POWER
, also available as the (now
deprecated) function igraph_pagerank_old()
. The second
implementation is based on the ARPACK library, this was the default
before igraph version 0.7: IGRAPH_PAGERANK_ALGO_ARPACK
.
The third and recommmended implementation is IGRAPH_PAGERANK_ALGO_PRPACK
. This is using the the PRPACK package,
see https://github.com/dgleich/prpack .
Please note that the PageRank of a given vertex depends on the PageRank of all other vertices, so even if you want to calculate the PageRank for only some of the vertices, all of them must be calculated. Requesting the PageRank for only some of the vertices does not result in any performance increase at all.
For the explanation of the PageRank algorithm, see the following webpage: http://infolab.stanford.edu/~backrub/google.html , or the following reference:
Sergey Brin and Larry Page: The Anatomy of a LargeScale Hypertextual Web Search Engine. Proceedings of the 7th WorldWide Web Conference, Brisbane, Australia, April 1998.
Arguments:

The graph object. 

The PageRank implementation to use. Possible values:


Pointer to an initialized vector, the result is stored here. It is resized as needed. 

Pointer to a real variable, the eigenvalue corresponding to the PageRank vector is stored here. It should be always exactly one. 

The vertex ids for which the PageRank is returned. 

Boolean, whether to consider the directedness of the edges. This is ignored for undirected graphs. 

The damping factor ("d" in the original paper) 

Optional edge weights, it is either a null pointer, then the edges are not weighted, or a vector of the same length as the number of edges. 

Options to the power method or ARPACK. For the power
method, 
Returns:
Error code:

Time complexity: depends on the input graph, usually it is O(E), the number of edges.
See also:

Example 13.15. File examples/simple/igraph_pagerank.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <unistd.h> #include <libgen.h> void warning_handler_stdout (const char *reason, const char *file, int line, int igraph_errno) { IGRAPH_UNUSED(igraph_errno); printf("Warning: %s\n", reason); } void print_vector(igraph_vector_t *v, FILE *f) { long int i; for (i = 0; i < igraph_vector_size(v); i++) { fprintf(f, " %4.2f", VECTOR(*v)[i]); } fprintf(f, "\n"); } igraph_warning_handler_t *oldwarn; int main() { igraph_t g; igraph_vector_t v, res, reset, weights; igraph_arpack_options_t arpack_options; igraph_real_t value; int ret; igraph_pagerank_power_options_t power_options; /* Test graphs taken from http://www.iprcom.com/papers/pagerank/ */ igraph_vector_init(&v, 10); VECTOR(v)[0] = 0; VECTOR(v)[1] = 1; VECTOR(v)[2] = 1; VECTOR(v)[3] = 2; VECTOR(v)[4] = 2; VECTOR(v)[5] = 0; VECTOR(v)[6] = 3; VECTOR(v)[7] = 2; VECTOR(v)[8] = 0; VECTOR(v)[9] = 2; igraph_create(&g, &v, 0, 1); igraph_vector_init(&res, 0); oldwarn = igraph_set_warning_handler(warning_handler_stdout); igraph_pagerank_old(&g, &res, igraph_vss_all(), 1, 1000, 0.001, 0.85, 0); print_vector(&res, stdout); igraph_vector_destroy(&res); igraph_vector_destroy(&v); igraph_destroy(&g); igraph_vector_init(&v, 28); VECTOR(v)[ 0] = 0; VECTOR(v)[ 1] = 1; VECTOR(v)[ 2] = 0; VECTOR(v)[ 3] = 2; VECTOR(v)[ 4] = 0; VECTOR(v)[ 5] = 3; VECTOR(v)[ 6] = 1; VECTOR(v)[ 7] = 0; VECTOR(v)[ 8] = 2; VECTOR(v)[ 9] = 0; VECTOR(v)[10] = 3; VECTOR(v)[11] = 0; VECTOR(v)[12] = 3; VECTOR(v)[13] = 4; VECTOR(v)[14] = 3; VECTOR(v)[15] = 5; VECTOR(v)[16] = 3; VECTOR(v)[17] = 6; VECTOR(v)[18] = 3; VECTOR(v)[19] = 7; VECTOR(v)[20] = 4; VECTOR(v)[21] = 0; VECTOR(v)[22] = 5; VECTOR(v)[23] = 0; VECTOR(v)[24] = 6; VECTOR(v)[25] = 0; VECTOR(v)[26] = 7; VECTOR(v)[27] = 0; igraph_create(&g, &v, 0, 1); igraph_vector_init(&res, 0); igraph_pagerank_old(&g, &res, igraph_vss_all(), 1, 10000, 0.0001, 0.85, 0); print_vector(&res, stdout); igraph_vector_destroy(&res); igraph_vector_destroy(&v); igraph_destroy(&g); igraph_set_warning_handler(oldwarn); /* New PageRank */ igraph_star(&g, 11, IGRAPH_STAR_UNDIRECTED, 0); igraph_vector_init(&res, 0); igraph_arpack_options_init(&arpack_options); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, 0, igraph_vss_all(), 0, 0.85, 0, &arpack_options); print_vector(&res, stdout); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, 0, igraph_vss_all(), 0, 0.85, 0, 0); print_vector(&res, stdout); /* Check twice more for consistency */ igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, 0, igraph_vss_all(), 0, 0.85, 0, &arpack_options); print_vector(&res, stdout); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, 0, igraph_vss_all(), 0, 0.85, 0, 0); print_vector(&res, stdout); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, 0, igraph_vss_all(), 0, 0.85, 0, &arpack_options); print_vector(&res, stdout); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, 0, igraph_vss_all(), 0, 0.85, 0, 0); print_vector(&res, stdout); /* Check personalized PageRank */ igraph_personalized_pagerank_vs(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, 0, igraph_vss_all(), 0, 0.5, igraph_vss_1(1), 0, &arpack_options); print_vector(&res, stdout); igraph_personalized_pagerank_vs(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, 0, igraph_vss_all(), 0, 0.5, igraph_vss_1(1), 0, 0); print_vector(&res, stdout); /* Errors */ power_options.niter = 1; power_options.eps = 0.0001; igraph_set_error_handler(igraph_error_handler_ignore); igraph_set_warning_handler(igraph_warning_handler_ignore); ret = igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_POWER, &res, /*value=*/ 0, igraph_vss_all(), 1, 0.85, /*weights=*/ 0, &power_options); if (ret != IGRAPH_EINVAL) { return 1; } power_options.niter = 10000; power_options.eps = 1; ret = igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_POWER, &res, /*value=*/ 0, igraph_vss_all(), 1, 0.85, /*weights=*/ 0, &power_options); if (ret != IGRAPH_EINVAL) { return 2; } power_options.niter = 10000; power_options.eps = 0.0001; ret = igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_POWER, &res, /*value=*/ 0, igraph_vss_all(), 1, 1.2, /*weights=*/ 0, &power_options); if (ret != IGRAPH_EINVAL) { return 3; } igraph_vector_init(&reset, 2); ret = igraph_personalized_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, 0, igraph_vss_all(), 0, 0.85, &reset, 0, &arpack_options); if (ret != IGRAPH_EINVAL) { return 4; } ret = igraph_personalized_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, 0, igraph_vss_all(), 0, 0.85, &reset, 0, 0); if (ret != IGRAPH_EINVAL) { return 4; } igraph_vector_resize(&reset, 10); igraph_vector_fill(&reset, 0); ret = igraph_personalized_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, 0, igraph_vss_all(), 0, 0.85, &reset, 0, &arpack_options); if (ret != IGRAPH_EINVAL) { return 5; } ret = igraph_personalized_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, 0, igraph_vss_all(), 0, 0.85, &reset, 0, 0); if (ret != IGRAPH_EINVAL) { return 5; } igraph_vector_destroy(&reset); igraph_destroy(&g); igraph_set_error_handler(igraph_error_handler_abort); /* Special cases: check for empty graph */ igraph_empty(&g, 10, 0); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, &value, igraph_vss_all(), 1, 0.85, 0, &arpack_options); if (value != 1.0) { return 6; } igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, &value, igraph_vss_all(), 1, 0.85, 0, 0); if (value != 1.0) { return 6; } print_vector(&res, stdout); igraph_destroy(&g); /* Special cases: check for full graph, zero weights */ igraph_full(&g, 10, 0, 0); igraph_vector_init(&v, 45); igraph_vector_fill(&v, 0); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, &value, igraph_vss_all(), 1, 0.85, &v, &arpack_options); if (value != 1.0) { return 7; } igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, &value, igraph_vss_all(), 1, 0.85, &v, 0); if (value != 1.0) { return 7; } igraph_vector_destroy(&v); print_vector(&res, stdout); igraph_destroy(&g); /* Another test case for PageRank (bug #792352) */ igraph_small(&g, 9, 1, 0, 5, 1, 5, 2, 0, 3, 1, 5, 4, 5, 7, 6, 0, 8, 0, 8, 1, 1); igraph_vector_init(&weights, 9); VECTOR(weights)[0] = 4; VECTOR(weights)[1] = 5; VECTOR(weights)[2] = 5; VECTOR(weights)[3] = 4; VECTOR(weights)[4] = 4; VECTOR(weights)[5] = 4; VECTOR(weights)[6] = 3; VECTOR(weights)[7] = 4; VECTOR(weights)[8] = 4; igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_ARPACK, &res, 0, igraph_vss_all(), 1, 0.85, &weights, &arpack_options); print_vector(&res, stdout); igraph_pagerank(&g, IGRAPH_PAGERANK_ALGO_PRPACK, &res, 0, igraph_vss_all(), 1, 0.85, &weights, 0); print_vector(&res, stdout); igraph_vector_destroy(&weights); igraph_destroy(&g); igraph_vector_destroy(&res); return 0; }
int igraph_pagerank_old(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, igraph_bool_t directed, igraph_integer_t niter, igraph_real_t eps, igraph_real_t damping, igraph_bool_t old);
This is an old implementation,
it is provided for compatibility with igraph versions earlier than
0.5. Please use the new implementation igraph_pagerank()
in
new projects.
From version 0.7 this function is deprecated and its use gives a warning message.
Please note that the PageRank of a given vertex depends on the PageRank of all other vertices, so even if you want to calculate the PageRank for only some of the vertices, all of them must be calculated. Requesting the PageRank for only some of the vertices does not result in any performance increase at all.
Since the calculation is an iterative process, the algorithm is stopped after a given count of iterations or if the PageRank value differences between iterations are less than a predefined value.
For the explanation of the PageRank algorithm, see the following webpage: http://infolab.stanford.edu/~backrub/google.html , or the following reference:
Sergey Brin and Larry Page: The Anatomy of a LargeScale Hypertextual Web Search Engine. Proceedings of the 7th WorldWide Web Conference, Brisbane, Australia, April 1998.
Arguments:

The graph object. 

The result vector containing the PageRank values for the given nodes. 

Vector with the vertex ids 

Logical, if true directed paths will be considered for directed graphs. It is ignored for undirected graphs. 

The maximum number of iterations to perform 

The algorithm will consider the calculation as complete if the difference of PageRank values between iterations change less than this value for every node 

The damping factor ("d" in the original paper) 

Boolean, whether to use the preigraph 0.5 way to calculate page rank. Not recommended for new applications, only included for compatibility. If this is nonzero then the damping factor is not divided by the number of vertices before adding it to the weighted page rank scores to calculate the new scores. I.e. the formula in the original PageRank paper is used. Furthermore, if this is nonzero then the PageRank vector is renormalized after each iteration. 
Returns:
Error code:

Time complexity: O(V+E) per iteration. A handful iterations should be enough. Note that if the oldstyle dumping is used then the iteration might not converge at all.
See also:

int igraph_personalized_pagerank(const igraph_t *graph, igraph_pagerank_algo_t algo, igraph_vector_t *vector, igraph_real_t *value, const igraph_vs_t vids, igraph_bool_t directed, igraph_real_t damping, igraph_vector_t *reset, const igraph_vector_t *weights, void *options);
The personalized PageRank is similar to the original PageRank measure, but the random walk is reset in every step with probability 1damping to a nonuniform distribution (instead of the uniform distribution in the original PageRank measure.
Please note that the personalized PageRank of a given vertex depends on the personalized PageRank of all other vertices, so even if you want to calculate the personalized PageRank for only some of the vertices, all of them must be calculated. Requesting the personalized PageRank for only some of the vertices does not result in any performance increase at all.
Arguments:

The graph object. 

The PageRank implementation to use. Possible values:


Pointer to an initialized vector, the result is stored here. It is resized as needed. 

Pointer to a real variable, the eigenvalue corresponding to the PageRank vector is stored here. It should be always exactly one. 

The vertex ids for which the PageRank is returned. 

Boolean, whether to consider the directedness of the edges. This is ignored for undirected graphs. 

The damping factor ("d" in the original paper) 

The probability distribution over the vertices used when resetting the random walk. It is either a null pointer (denoting a uniform choice that results in the original PageRank measure) or a vector of the same length as the number of vertices. 

Optional edge weights, it is either a null pointer, then the edges are not weighted, or a vector of the same length as the number of edges. 

Options to the power method or ARPACK. For the power
method, 
Returns:
Error code:

Time complexity: depends on the input graph, usually it is O(E), the number of edges.
See also:

int igraph_personalized_pagerank_vs(const igraph_t *graph, igraph_pagerank_algo_t algo, igraph_vector_t *vector, igraph_real_t *value, const igraph_vs_t vids, igraph_bool_t directed, igraph_real_t damping, igraph_vs_t reset_vids, const igraph_vector_t *weights, void *options);
The personalized PageRank is similar to the original PageRank measure, but the random walk is reset in every step with probability 1damping to a nonuniform distribution (instead of the uniform distribution in the original PageRank measure.
This simplified interface takes a vertex sequence and resets the random walk to
one of the vertices in the specified vertex sequence, chosen uniformly. A typical
application of personalized PageRank is when the random walk is reset to the same
vertex every time  this can easily be achieved using igraph_vss_1()
which
generates a vertex sequence containing only a single vertex.
Please note that the personalized PageRank of a given vertex depends on the personalized PageRank of all other vertices, so even if you want to calculate the personalized PageRank for only some of the vertices, all of them must be calculated. Requesting the personalized PageRank for only some of the vertices does not result in any performance increase at all.
Arguments:

The graph object. 

The PageRank implementation to use. Possible values:


Pointer to an initialized vector, the result is stored here. It is resized as needed. 

Pointer to a real variable, the eigenvalue corresponding to the PageRank vector is stored here. It should be always exactly one. 

The vertex ids for which the PageRank is returned. 

Boolean, whether to consider the directedness of the edges. This is ignored for undirected graphs. 

The damping factor ("d" in the original paper) 

IDs of the vertices used when resetting the random walk. 

Optional edge weights, it is either a null pointer, then the edges are not weighted, or a vector of the same length as the number of edges. 

Options to the power method or ARPACK. For the power
method, 
Returns:
Error code:

Time complexity: depends on the input graph, usually it is O(E), the number of edges.
See also:

int igraph_constraint(const igraph_t *graph, igraph_vector_t *res, igraph_vs_t vids, const igraph_vector_t *weights);
This function calculates Burt's constraint scores for the given vertices, also known as structural holes.
Burt's constraint is higher if ego has less, or mutually stronger related (i.e. more redundant) contacts. Burt's measure of constraint, C[i], of vertex i's ego network V[i], is defined for directed and valued graphs,
C[i] = sum( sum( (p[i,q] p[q,j])^2, q in V[i], q != i,j ), j in V[], j != i)
for a graph of order (ie. number of vertices) N, where proportional tie strengths are defined as
p[i,j]=(a[i,j]+a[j,i]) / sum(a[i,k]+a[k,i], k in V[i], k != i),
a[i,j] are elements of A and the latter being the graph adjacency matrix. For isolated vertices, constraint is undefined.
Burt, R.S. (2004). Structural holes and good ideas. American Journal of Sociology 110, 349399.
The first R version of this function was contributed by Jeroen Bruggeman.
Arguments:

A graph object. 

Pointer to an initialized vector, the result will be stored here. The vector will be resized to have the appropriate size for holding the result. 

Vertex selector containing the vertices for which the constraint should be calculated. 

Vector giving the weights of the edges. If it is

Returns:
Error code. 
Time complexity: O(V+E+n*d^2), n is the number of vertices for
which the constraint is calculated and d is the average degree, V
is the number of vertices, E the number of edges in the
graph. If the weights argument is NULL
then the time complexity
is O(V+n*d^2).
int igraph_maxdegree(const igraph_t *graph, igraph_integer_t *res, igraph_vs_t vids, igraph_neimode_t mode, igraph_bool_t loops);
The largest in, out or total degree of the specified vertices is calculated.
Arguments:

The input graph. 

Pointer to an integer ( 

Vector giving the vertex IDs for which the maximum degree will be calculated. 

Defines the type of the degree.


Boolean, gives whether the selfloops should be counted. 
Returns:
Error code:

Time complexity: O(v) if loops is TRUE, and O(v*d) otherwise. v is the number vertices for which the degree will be calculated, and d is their (average) degree.
int igraph_strength(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, igraph_neimode_t mode, igraph_bool_t loops, const igraph_vector_t *weights);
In a weighted network the strength of a vertex is the sum of the weights of all incident edges. In a nonweighted network this is exactly the vertex degree.
Arguments:

The input graph. 

Pointer to an initialized vector, the result is stored here. It will be resized as needed. 

The vertices for which the calculation is performed. 

Gives whether to count only outgoing ( 

A logical scalar, whether to count loop edges as well. 

A vector giving the edge weights. If this is a NULL
pointer, then 
Returns:
Error code. 
Time complexity: O(V+E), linear in the number vertices and edges.
See also:

int igraph_eigenvector_centrality(const igraph_t *graph, igraph_vector_t *vector, igraph_real_t *value, igraph_bool_t directed, igraph_bool_t scale, const igraph_vector_t *weights, igraph_arpack_options_t *options);
Eigenvector centrality is a measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to highscoring nodes contribute more to the score of the node in question than equal connections to lowscoring nodes. In practice, this is determined by calculating the eigenvector corresponding to the largest positive eigenvalue of the adjacency matrix. The centrality scores returned by igraph are always normalized such that the largest eigenvector centrality score is one (with one exception, see below).
Since the eigenvector centrality scores of nodes in different components do not affect each other, it may be beneficial for large graphs to decompose it first into weakly connected components and calculate the centrality scores individually for each component.
Also note that the adjacency matrix of a directed acyclic graph or the
adjacency matrix of an empty graph does not possess positive eigenvalues,
therefore the eigenvector centrality is not defined for these graphs.
igraph will return an eigenvalue of zero in such cases. The eigenvector
centralities will all be equal for an empty graph and will all be zeros
for a directed acyclic graph. Such pathological cases can be detected
by asking igraph to calculate the eigenvalue as well (using the value
parameter, see below) and checking whether the eigenvalue is very close
to zero.
Arguments:

The input graph. It might be directed. 

Pointer to an initialized vector, it will be resized as needed. The result of the computation is stored here. It can be a null pointer, then it is ignored. 

If not a null pointer, then the eigenvalue corresponding to the found eigenvector is stored here. 

Boolean scalar, whether to consider edge directions in a directed graph. It is ignored for undirected graphs. 

If not zero then the result will be scaled such that the absolute value of the maximum centrality is one. 

A null pointer (=no edge weights), or a vector giving the weights of the edges. The algorithm might result complex numbers is some weights are negative. In this case only the real part is reported. 

Options to ARPACK. See 
Returns:
Error code. 
Time complexity: depends on the input graph, usually it is O(V+E).
See also:

Example 13.16. File examples/simple/eigenvector_centrality.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20072012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include "igraph.h" #include <math.h> int main() { igraph_t g; igraph_vector_t v, weights; long int i; igraph_real_t value; igraph_arpack_options_t options; igraph_star(&g, 100, IGRAPH_STAR_UNDIRECTED, 0); igraph_arpack_options_init(&options); igraph_vector_init(&v, 0); igraph_eigenvector_centrality(&g, &v, &value, /*directed=*/ 0, /*scale=*/0, /*weights=*/0, &options); if (options.info != 0) { return 1; } for (i = 0; i < igraph_vector_size(&v); i++) { printf(" %.3f", fabs(VECTOR(v)[i])); } printf("\n"); igraph_destroy(&g); /* Special cases: check for empty graph */ igraph_empty(&g, 10, 0); igraph_eigenvector_centrality(&g, &v, &value, 0, 0, 0, &options); if (value != 0.0) { return 1; } for (i = 0; i < igraph_vector_size(&v); i++) { printf(" %.2f", fabs(VECTOR(v)[i])); } printf("\n"); igraph_destroy(&g); /* Special cases: check for full graph, zero weights */ igraph_full(&g, 10, 0, 0); igraph_vector_init(&weights, 45); igraph_vector_fill(&weights, 0); igraph_eigenvector_centrality(&g, &v, &value, 0, 0, &weights, &options); igraph_vector_destroy(&weights); if (value != 0.0) { return 2; } for (i = 0; i < igraph_vector_size(&v); i++) { printf(" %.2f", fabs(VECTOR(v)[i])); } printf("\n"); igraph_destroy(&g); igraph_vector_destroy(&v); return 0; }
int igraph_hub_score(const igraph_t *graph, igraph_vector_t *vector, igraph_real_t *value, igraph_bool_t scale, const igraph_vector_t *weights, igraph_arpack_options_t *options);
The hub scores of the vertices are defined as the principal
eigenvector of A*A^T
, where A
is the adjacency
matrix of the graph, A^T
is its transposed.
See the following reference on the meaning of this score: J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACMSIAM Symposium on Discrete Algorithms, 1998. Extended version in Journal of the ACM 46(1999). Also appears as IBM Research Report RJ 10076, May 1997.
Arguments:

The input graph. Can be directed and undirected. 

Pointer to an initialized vector, the result is stored here. If a null pointer then it is ignored. 

If not a null pointer then the eigenvalue corresponding to the calculated eigenvector is stored here. 

If not zero then the result will be scaled such that the absolute value of the maximum centrality is one. 

A null pointer (=no edge weights), or a vector giving the weights of the edges. 

Options to ARPACK. See 
Returns:
Error code. 
Time complexity: depends on the input graph, usually it is O(V), the number of vertices.
See also:

int igraph_authority_score(const igraph_t *graph, igraph_vector_t *vector, igraph_real_t *value, igraph_bool_t scale, const igraph_vector_t *weights, igraph_arpack_options_t *options);
The authority scores of the vertices are defined as the principal
eigenvector of A^T*A
, where A
is the adjacency
matrix of the graph, A^T
is its transposed.
See the following reference on the meaning of this score: J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACMSIAM Symposium on Discrete Algorithms, 1998. Extended version in Journal of the ACM 46(1999). Also appears as IBM Research Report RJ 10076, May 1997.
Arguments:

The input graph. Can be directed and undirected. 

Pointer to an initialized vector, the result is stored here. If a null pointer then it is ignored. 

If not a null pointer then the eigenvalue corresponding to the calculated eigenvector is stored here. 

If not zero then the result will be scaled such that the absolute value of the maximum centrality is one. 

A null pointer (=no edge weights), or a vector giving the weights of the edges. 

Options to ARPACK. See 
Returns:
Error code. 
Time complexity: depends on the input graph, usually it is O(V), the number of vertices.
See also:

int igraph_closeness_estimate(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, igraph_neimode_t mode, igraph_real_t cutoff, const igraph_vector_t *weights, igraph_bool_t normalized);
The closeness centrality of a vertex measures how easily other vertices can be reached from it (or the other way: how easily it can be reached from the other vertices). It is defined as the number of vertices minus one divided by the sum of the lengths of all geodesics from/to the given vertex. When estimating closeness centrality, igraph considers paths having a length less than or equal to a prescribed cutoff value.
If the graph is not connected, and there is no such path between two vertices, the number of vertices is used instead the length of the geodesic. This is always longer than the longest possible geodesic.
Since the estimation considers vertex pairs with a distance greater than the given value as disconnected, the resulting estimation will always be lower than the actual closeness centrality.
Arguments:

The graph object. 


The result of the computation, a vector containing the closeness centrality scores for the given vertices. 


The vertices for which the closeness centrality will be estimated. 


The type of shortest paths to be used for the calculation in directed graphs. Possible values:



The maximal length of paths that will be considered. If zero or negative, the exact closeness will be calculated (no upper limit on path lengths). 


An optional vector containing edge weights for weighted closeness. Supply a null pointer here for traditional, unweighted closeness. 


Boolean, whether to normalize results by multiplying by the number of vertices minus one. 
Returns:
Error code:

Time complexity: O(nE), n is the number of vertices for which the calculation is done and E is the number of edges in the graph.
See also:
Other centrality types: 
int igraph_betweenness_estimate(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, igraph_bool_t directed, igraph_real_t cutoff, const igraph_vector_t *weights, igraph_bool_t nobigint);
The betweenness centrality of a vertex is the number of geodesics going through it. If there are more than one geodesic between two vertices, the value of these geodesics are weighted by one over the number of geodesics. When estimating betweenness centrality, igraph takes into consideration only those paths that are shorter than or equal to a prescribed length. Note that the estimated centrality will always be less than the real one.
Arguments:

The graph object. 

The result of the computation, a vector containing the estimated betweenness scores for the specified vertices. 

The vertices of which the betweenness centrality scores will be estimated. 

Logical, if true directed paths will be considered for directed graphs. It is ignored for undirected graphs. 

The maximal length of paths that will be considered. If zero or negative, the exact betweenness will be calculated (no upper limit on path lengths). 

An optional vector containing edge weights for calculating weighted betweenness. Supply a null pointer here for unweighted betweenness. 

Logical, if true, then we don't use big integers for the calculation, setting this to 1 (=true) should work for most graphs. It is currently ignored for weighted graphs. 
Returns:
Error code:

Time complexity: O(VE), V and E are the number of vertices and edges in the graph. Note that the time complexity is independent of the number of vertices for which the score is calculated.
See also:
Other centrality types: 
int igraph_edge_betweenness_estimate(const igraph_t *graph, igraph_vector_t *result, igraph_bool_t directed, igraph_real_t cutoff, const igraph_vector_t *weights);
The betweenness centrality of an edge is the number of geodesics going through it. If there are more than one geodesics between two vertices, the value of these geodesics are weighted by one over the number of geodesics. When estimating betweenness centrality, igraph takes into consideration only those paths that are shorter than or equal to a prescribed length. Note that the estimated centrality will always be less than the real one.
Arguments:

The graph object. 

The result of the computation, vector containing the betweenness scores for the edges. 

Logical, if true directed paths will be considered for directed graphs. It is ignored for undirected graphs. 

The maximal length of paths that will be considered. If zero or negative, the exact betweenness will be calculated (no upper limit on path lengths). 

An optional weight vector for weighted betweenness. Supply a null pointer here for unweighted betweenness. 
Returns:
Error code:

Time complexity: O(VE), V and E are the number of vertices and edges in the graph.
See also:
Other centrality types: 
igraph_centralization
— Calculate the centralization score from the node level scoresigraph_centralization_degree
— Calculate vertex degree and graph centralizationigraph_centralization_betweenness
— Calculate vertex betweenness and graph centralizationigraph_centralization_closeness
— Calculate vertex closeness and graph centralizationigraph_centralization_eigenvector_centrality
— Calculate eigenvector centrality scores and graph centralizationigraph_centralization_degree_tmax
— Theoretical maximum for graph centralization based on degreeigraph_centralization_betweenness_tmax
— Theoretical maximum for graph centralization based on betweennessigraph_centralization_closeness_tmax
— Theoretical maximum for graph centralization based on closenessigraph_centralization_eigenvector_centrality_tmax
— Theoretical maximum centralization for eigenvector centrality
igraph_real_t igraph_centralization(const igraph_vector_t *scores, igraph_real_t theoretical_max, igraph_bool_t normalized);
For a centrality score defined on the vertices of a graph, it is possible to define a graph level centralization index, by calculating the sum of the deviation from the maximum centrality score. Consequently, the higher the centralization index of the graph, the more centralized the structure is.
In order to make graphs of different sizes comparable, the centralization index is usually normalized to a number between zero and one, by dividing the (unnormalized) centralization score of the most centralized structure with the same number of vertices.
For most centrality indices the most centralized structure is the star graph, a single center connected to all other nodes in the network. There are some variation depending on whether the graph is directed or not, whether loop edges are allowed, etc.
This function simply calculates the graph level index, if the node level scores and the theoretical maximum are given. It is called by all the measurespecific centralization functions.
Arguments:

A vector containing the nodelevel centrality scores. 

The graph level centrality score of the most
centralized graph with the same number of vertices. Only used
if 

Boolean, whether to normalize the centralization by dividing the supplied theoretical maximum. 
Returns:
The graph level index. 
See also:

Time complexity: O(n), the length of the score vector.
Example 13.17. File examples/simple/centralization.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20092012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <math.h> #define ALMOST_EQUALS(a, b) (fabs((a)(b)) < 1e8) int main() { igraph_t g; igraph_real_t cent; igraph_arpack_options_t arpack_options; /****************************/ /* instar */ igraph_star(&g, 10, IGRAPH_STAR_IN, /*center=*/ 0); igraph_centralization_degree(&g, /*res=*/ 0, /*mode=*/ IGRAPH_IN, IGRAPH_NO_LOOPS, ¢, /*theoretical_max=*/ 0, /*normalized=*/ 1); if (cent != 1.0) { fprintf(stderr, "instar, degree: %g\n", cent); return 1; } igraph_centralization_betweenness(&g, /*res=*/ 0, IGRAPH_UNDIRECTED, /*nobigint=*/ 1, ¢, /*theoretical_max=*/ 0, /*normalized=*/ 1); if (cent != 1.0) { fprintf(stderr, "instar, betweenness: %g\n", cent); return 2; } igraph_set_warning_handler(igraph_warning_handler_ignore); igraph_centralization_closeness(&g, /*res=*/ 0, IGRAPH_IN, ¢, /*theoretical_max=*/ 0, /*normalization=*/ 1); igraph_set_warning_handler(igraph_warning_handler_print); if (!ALMOST_EQUALS(cent, 1.0)) { fprintf(stderr, "instar, closeness: %g\n", cent); return 3; } igraph_destroy(&g); /****************************/ /* outstar */ igraph_star(&g, 10, IGRAPH_STAR_OUT, /*center=*/ 0); igraph_centralization_degree(&g, /*res=*/ 0, /*mode=*/ IGRAPH_OUT, IGRAPH_NO_LOOPS, ¢, /*theoretical_max=*/ 0, /*normalized=*/ 1); if (cent != 1.0) { fprintf(stderr, "outstar, degree: %g\n", cent); return 11; } igraph_centralization_betweenness(&g, /*res=*/ 0, IGRAPH_UNDIRECTED, /*nobigint=*/ 1, ¢, /*theoretical_max=*/ 0, /*normalized=*/ 1); if (cent != 1.0) { fprintf(stderr, "outstar, betweenness: %g\n", cent); return 12; } igraph_set_warning_handler(igraph_warning_handler_ignore); igraph_centralization_closeness(&g, /*res=*/ 0, IGRAPH_OUT, ¢, /*theoretical_max=*/ 0, /*normalization=*/ 1); igraph_set_warning_handler(igraph_warning_handler_print); if (!ALMOST_EQUALS(cent, 1.0)) { fprintf(stderr, "outstar, closeness: %g\n", cent); return 13; } igraph_destroy(&g); /****************************/ /* undricted star */ igraph_star(&g, 10, IGRAPH_STAR_UNDIRECTED, /*center=*/ 0); igraph_centralization_degree(&g, /*res=*/ 0, /*mode=*/ IGRAPH_ALL, IGRAPH_NO_LOOPS, ¢, /*theoretical_max=*/ 0, /*normalized=*/ 1); if (cent != 1.0) { fprintf(stderr, "undirected star, degree: %g\n", cent); return 21; } igraph_centralization_betweenness(&g, /*res=*/ 0, IGRAPH_UNDIRECTED, /*nobigint=*/ 1, ¢, /*theoretical_max=*/ 0, /*normalized=*/ 1); if (cent != 1.0) { fprintf(stderr, "undirected star, betweenness: %g\n", cent); return 22; } igraph_centralization_closeness(&g, /*res=*/ 0, IGRAPH_ALL, ¢, /*theoretical_max=*/ 0, /*normalization=*/ 1); if (!ALMOST_EQUALS(cent, 1.0)) { fprintf(stderr, "undirected star, closeness: %g\n", cent); return 23; } igraph_destroy(&g); /****************************/ /* single dyad */ igraph_small(&g, /*n=*/ 10, /*directed=*/ 0, 0, 1, 1); igraph_arpack_options_init(&arpack_options); igraph_centralization_eigenvector_centrality(&g, /*vector=*/ 0, /*value=*/ 0, /*directed=*/ 1, /*scale=*/ 1, &arpack_options, ¢, /*theoretical_max=*/ 0, /*normalization=*/ 1); if (!ALMOST_EQUALS(cent, 1.0)) { fprintf(stderr, "dyad, eigenvector centrality: %g\n", cent); return 24; } igraph_centralization_eigenvector_centrality(&g, /*vector=*/ 0, /*value=*/ 0, /*directed=*/ 1, /*scale=*/ 0, &arpack_options, ¢, /*theoretical_max=*/ 0, /*normalization=*/ 1); if (!ALMOST_EQUALS(cent, 1.0)) { fprintf(stderr, "dyad, eigenvector centrality, not scaled: %g\n", cent); return 25; } igraph_destroy(&g); return 0; }
int igraph_centralization_degree(const igraph_t *graph, igraph_vector_t *res, igraph_neimode_t mode, igraph_bool_t loops, igraph_real_t *centralization, igraph_real_t *theoretical_max, igraph_bool_t normalized);
This function calculates the degree of the vertices by passing its
arguments to igraph_degree()
; and it calculates the graph
level centralization index based on the results by calling igraph_centralization()
.
Arguments:

The input graph. 

A vector if you need the nodelevel degree scores, or a null pointer otherwise. 

Constant the specifies the type of degree for directed
graphs. Possible values: 

Boolean, whether to consider loop edges when calculating the degree (and the centralization). 

Pointer to a real number, the centralization score is placed here. 

Pointer to real number or a null pointer. If not a null pointer, then the theoretical maximum graph centrality score for a graph with the same number vertices is stored here. 

Boolean, whether to calculate a normalized
centralization score. See 
Returns:
Error code. 
See also:
Time complexity: the complexity of igraph_degree()
plus O(n),
the number of vertices queried, for calculating the centralization
score.
int igraph_centralization_betweenness(const igraph_t *graph, igraph_vector_t *res, igraph_bool_t directed, igraph_bool_t nobigint, igraph_real_t *centralization, igraph_real_t *theoretical_max, igraph_bool_t normalized);
This function calculates the betweenness centrality of the vertices
by passing its arguments to igraph_betweenness()
; and it
calculates the graph level centralization index based on the
results by calling igraph_centralization()
.
Arguments:

The input graph. 

A vector if you need the nodelevel betweenness scores, or a null pointer otherwise. 

Boolean, whether to consider directed paths when calculating betweenness. 

Logical, if true, then we don't use big integers for the calculation, setting this to zero (=false) should work for most graphs. It is currently ignored for weighted graphs. 

Pointer to a real number, the centralization score is placed here. 

Pointer to real number or a null pointer. If not a null pointer, then the theoretical maximum graph centrality score for a graph with the same number vertices is stored here. 

Boolean, whether to calculate a normalized
centralization score. See 
Returns:
Error code. 
See also:
Time complexity: the complexity of igraph_betweenness()
plus
O(n), the number of vertices queried, for calculating the
centralization score.
int igraph_centralization_closeness(const igraph_t *graph, igraph_vector_t *res, igraph_neimode_t mode, igraph_real_t *centralization, igraph_real_t *theoretical_max, igraph_bool_t normalized);
This function calculates the closeness centrality of the vertices
by passing its arguments to igraph_closeness()
; and it
calculates the graph level centralization index based on the
results by calling igraph_centralization()
.
Arguments:

The input graph. 

A vector if you need the nodelevel closeness scores, or a null pointer otherwise. 

Constant the specifies the type of closeness for directed
graphs. Possible values: 

Pointer to a real number, the centralization score is placed here. 

Pointer to real number or a null pointer. If not a null pointer, then the theoretical maximum graph centrality score for a graph with the same number vertices is stored here. 

Boolean, whether to calculate a normalized
centralization score. See 
Returns:
Error code. 
See also:
Time complexity: the complexity of igraph_closeness()
plus
O(n), the number of vertices queried, for calculating the
centralization score.
int igraph_centralization_eigenvector_centrality( const igraph_t *graph, igraph_vector_t *vector, igraph_real_t *value, igraph_bool_t directed, igraph_bool_t scale, igraph_arpack_options_t *options, igraph_real_t *centralization, igraph_real_t *theoretical_max, igraph_bool_t normalized);
This function calculates the eigenvector centrality of the vertices
by passing its arguments to igraph_eigenvector_centrality
);
and it calculates the graph level centralization index based on the
results by calling igraph_centralization()
.
Arguments:

The input graph. 

A vector if you need the nodelevel eigenvector centrality scores, or a null pointer otherwise. 

If not a null pointer, then the leading eigenvalue is stored here. 

If not zero then the result will be scaled, such that the absolute value of the maximum centrality is one. 

Options to ARPACK. See 

Pointer to a real number, the centralization score is placed here. 

Pointer to real number or a null pointer. If not a null pointer, then the theoretical maximum graph centrality score for a graph with the same number vertices is stored here. 

Boolean, whether to calculate a normalized
centralization score. See 
Returns:
Error code. 
See also:
Time complexity: the complexity of igraph_eigenvector_centrality()
plus O(V), the number of vertices
for the calculating the centralization.
int igraph_centralization_degree_tmax(const igraph_t *graph, igraph_integer_t nodes, igraph_neimode_t mode, igraph_bool_t loops, igraph_real_t *res);
This function returns the theoretical maximum graph centrality based on vertex degree.
There are two ways to call this function, the first is to supply a
graph as the graph
argument, and then the number of
vertices is taken from this object, and its directedness is
considered as well. The nodes
argument is ignored in
this case. The mode
argument is also ignored if the
supplied graph is undirected.
The other way is to supply a null pointer as the graph
argument. In this case the nodes
and mode
arguments are considered.
The most centralized structure is the star. More specifically, for undirected graphs it is the star, for directed graphs it is the instar or the outstar.
Arguments:

A graph object or a null pointer, see the description above. 

The number of nodes. This is ignored if the


Constant, whether the calculation is based on indegree
( 

Boolean scalar, whether to consider loop edges in the calculation. 

Pointer to a real variable, the result is stored here. 
Returns:
Error code. 
Time complexity: O(1).
See also:
int igraph_centralization_betweenness_tmax(const igraph_t *graph, igraph_integer_t nodes, igraph_bool_t directed, igraph_real_t *res);
This function returns the theoretical maximum graph centrality based on vertex betweenness.
There are two ways to call this function, the first is to supply a
graph as the graph
argument, and then the number of
vertices is taken from this object, and its directedness is
considered as well. The nodes
argument is ignored in
this case. The directed
argument is also ignored if the
supplied graph is undirected.
The other way is to supply a null pointer as the graph
argument. In this case the nodes
and directed
arguments are considered.
The most centralized structure is the star.
Arguments:

A graph object or a null pointer, see the description above. 

The number of nodes. This is ignored if the


Boolean scalar, whether to use directed paths in
the betweenness calculation. This argument is ignored if


Pointer to a real variable, the result is stored here. 
Returns:
Error code. 
Time complexity: O(1).
See also:
int igraph_centralization_closeness_tmax(const igraph_t *graph, igraph_integer_t nodes, igraph_neimode_t mode, igraph_real_t *res);
This function returns the theoretical maximum graph centrality based on vertex closeness.
There are two ways to call this function, the first is to supply a
graph as the graph
argument, and then the number of
vertices is taken from this object, and its directedness is
considered as well. The nodes
argument is ignored in
this case. The mode
argument is also ignored if the
supplied graph is undirected.
The other way is to supply a null pointer as the graph
argument. In this case the nodes
and mode
arguments are considered.
The most centralized structure is the star.
Arguments:

A graph object or a null pointer, see the description above. 

The number of nodes. This is ignored if the


Constant, specifies what kinf of distances to consider
to calculate closeness. See the 

Pointer to a real variable, the result is stored here. 
Returns:
Error code. 
Time complexity: O(1).
See also:
int igraph_centralization_eigenvector_centrality_tmax( const igraph_t *graph, igraph_integer_t nodes, igraph_bool_t directed, igraph_bool_t scale, igraph_real_t *res);
This function returns the theoretical maximum graph centrality based on vertex eigenvector centrality.
There are two ways to call this function, the first is to supply a
graph as the graph
argument, and then the number of
vertices is taken from this object, and its directedness is
considered as well. The nodes
argument is ignored in
this case. The directed
argument is also ignored if the
supplied graph is undirected.
The other way is to supply a null pointer as the graph
argument. In this case the nodes
and directed
arguments are considered.
The most centralized directed structure is the instar. The most centralized undirected structure is the graph with a single edge.
Arguments:

A graph object or a null pointer, see the description above. 

The number of nodes. This is ignored if the


Boolean scalar, whether to consider edge
directions. This argument is ignored if


Whether to rescale the nodelevel centrality scores to have a maximum of one. 

Pointer to a real variable, the result is stored here. 
Returns:
Error code. 
Time complexity: O(1).
See also:
igraph_bibcoupling
— Bibliographic coupling.igraph_cocitation
— Cocitation coupling.igraph_similarity_jaccard
— Jaccard similarity coefficient for the given vertices.igraph_similarity_jaccard_pairs
— Jaccard similarity coefficient for given vertex pairs.igraph_similarity_jaccard_es
— Jaccard similarity coefficient for a given edge selector.igraph_similarity_dice
— Dice similarity coefficient.igraph_similarity_dice_pairs
— Dice similarity coefficient for given vertex pairs.igraph_similarity_dice_es
— Dice similarity coefficient for a given edge selector.igraph_similarity_inverse_log_weighted
— Vertex similarity based on the inverse logarithm of vertex degrees.
int igraph_bibcoupling(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t vids);
The bibliographic coupling of two vertices is the number
of other vertices they both cite, igraph_bibcoupling()
calculates
this.
The bibliographic coupling score for each given vertex and all
other vertices in the graph will be calculated.
Arguments:

The graph object to analyze. 

Pointer to a matrix, the result of the calculation will
be stored here. The number of its rows is the same as the
number of vertex ids in 

The vertex ids of the vertices for which the calculation will be done. 
Returns:
Error code:

Time complexity: O(Vd^2), V is the number of vertices in the graph, d is the (maximum) degree of the vertices in the graph.
See also:
int igraph_cocitation(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t vids);
Two vertices are cocited if there is another vertex citing both of
them. igraph_cocitation()
simply counts how many times two vertices are
cocited.
The cocitation score for each given vertex and all other vertices
in the graph will be calculated.
Arguments:

The graph object to analyze. 

Pointer to a matrix, the result of the calculation will
be stored here. The number of its rows is the same as the
number of vertex ids in 

The vertex ids of the vertices for which the calculation will be done. 
Returns:
Error code:

Time complexity: O(Vd^2), V is the number of vertices in the graph, d is the (maximum) degree of the vertices in the graph.
See also:
Example 13.18. File examples/simple/igraph_cocitation.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %li", (long int) MATRIX(*m, i, j)); } fprintf(f, "\n"); } } int main() { igraph_t g; igraph_matrix_t m; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); igraph_bibcoupling(&g, &m, igraph_vss_all()); print_matrix(&m, stdout); igraph_cocitation(&g, &m, igraph_vss_all()); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
int igraph_similarity_jaccard(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t vids, igraph_neimode_t mode, igraph_bool_t loops);
The Jaccard similarity coefficient of two vertices is the number of common neighbors divided by the number of vertices that are neighbors of at least one of the two vertices being considered. This function calculates the pairwise Jaccard similarities for some (or all) of the vertices.
Arguments:

The graph object to analyze 


Pointer to a matrix, the result of the calculation will
be stored here. The number of its rows and columns is the same
as the number of vertex ids in 


The vertex ids of the vertices for which the calculation will be done. 


The type of neighbors to be used for the calculation in directed graphs. Possible values:



Whether to include the vertices themselves in the neighbor sets. 
Returns:
Error code:

Time complexity: O(V^2 d), V is the number of vertices in the vertex iterator given, d is the (maximum) degree of the vertices in the graph.
See also:

Example 13.19. File examples/simple/igraph_similarity.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %.2f", MATRIX(*m, i, j)); } fprintf(f, "\n"); } fprintf(f, "==========\n"); } int check_jaccard_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_jaccard(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_jaccard_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_jaccard_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int check_dice_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_dice(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_dice_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_dice_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int main() { igraph_t g; igraph_matrix_t m; int ret; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); ret = check_jaccard_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 1; } igraph_similarity_jaccard(&g, &m, igraph_vss_seq(1, 2), IGRAPH_ALL, 0); print_matrix(&m, stdout); ret = check_jaccard_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 3; } ret = check_jaccard_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 4; } ret = check_dice_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 5; } ret = check_dice_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 6; } ret = check_dice_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 7; } igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_ALL); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_OUT); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_IN); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
int igraph_similarity_jaccard_pairs(const igraph_t *graph, igraph_vector_t *res, const igraph_vector_t *pairs, igraph_neimode_t mode, igraph_bool_t loops);
The Jaccard similarity coefficient of two vertices is the number of common neighbors divided by the number of vertices that are neighbors of at least one of the two vertices being considered. This function calculates the pairwise Jaccard similarities for a list of vertex pairs.
Arguments:

The graph object to analyze 


Pointer to a vector, the result of the calculation will
be stored here. The number of elements is the same as the number
of pairs in 


A vector that contains the pairs for which the similarity will be calculated. Each pair is defined by two consecutive elements, i.e. the first and second element of the vector specifies the first pair, the third and fourth element specifies the second pair and so on. 


The type of neighbors to be used for the calculation in directed graphs. Possible values:



Whether to include the vertices themselves in the neighbor sets. 
Returns:
Error code:

Time complexity: O(nd), n is the number of pairs in the given vector, d is the (maximum) degree of the vertices in the graph.
See also:

Example 13.20. File examples/simple/igraph_similarity.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %.2f", MATRIX(*m, i, j)); } fprintf(f, "\n"); } fprintf(f, "==========\n"); } int check_jaccard_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_jaccard(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_jaccard_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_jaccard_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int check_dice_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_dice(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_dice_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_dice_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int main() { igraph_t g; igraph_matrix_t m; int ret; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); ret = check_jaccard_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 1; } igraph_similarity_jaccard(&g, &m, igraph_vss_seq(1, 2), IGRAPH_ALL, 0); print_matrix(&m, stdout); ret = check_jaccard_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 3; } ret = check_jaccard_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 4; } ret = check_dice_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 5; } ret = check_dice_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 6; } ret = check_dice_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 7; } igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_ALL); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_OUT); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_IN); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
int igraph_similarity_jaccard_es(const igraph_t *graph, igraph_vector_t *res, const igraph_es_t es, igraph_neimode_t mode, igraph_bool_t loops);
The Jaccard similarity coefficient of two vertices is the number of common neighbors divided by the number of vertices that are neighbors of at least one of the two vertices being considered. This function calculates the pairwise Jaccard similarities for the endpoints of edges in a given edge selector.
Arguments:

The graph object to analyze 


Pointer to a vector, the result of the calculation will
be stored here. The number of elements is the same as the number
of edges in 


An edge selector that specifies the edges to be included in the result. 


The type of neighbors to be used for the calculation in directed graphs. Possible values:



Whether to include the vertices themselves in the neighbor sets. 
Returns:
Error code:

Time complexity: O(nd), n is the number of edges in the edge selector, d is the (maximum) degree of the vertices in the graph.
See also:

Example 13.21. File examples/simple/igraph_similarity.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %.2f", MATRIX(*m, i, j)); } fprintf(f, "\n"); } fprintf(f, "==========\n"); } int check_jaccard_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_jaccard(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_jaccard_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_jaccard_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int check_dice_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_dice(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_dice_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_dice_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int main() { igraph_t g; igraph_matrix_t m; int ret; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); ret = check_jaccard_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 1; } igraph_similarity_jaccard(&g, &m, igraph_vss_seq(1, 2), IGRAPH_ALL, 0); print_matrix(&m, stdout); ret = check_jaccard_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 3; } ret = check_jaccard_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 4; } ret = check_dice_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 5; } ret = check_dice_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 6; } ret = check_dice_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 7; } igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_ALL); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_OUT); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_IN); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
int igraph_similarity_dice(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t vids, igraph_neimode_t mode, igraph_bool_t loops);
The Dice similarity coefficient of two vertices is twice the number of common neighbors divided by the sum of the degrees of the vertices. This function calculates the pairwise Dice similarities for some (or all) of the vertices.
Arguments:

The graph object to analyze 


Pointer to a matrix, the result of the calculation will
be stored here. The number of its rows and columns is the same
as the number of vertex ids in 


The vertex ids of the vertices for which the calculation will be done. 


The type of neighbors to be used for the calculation in directed graphs. Possible values:



Whether to include the vertices themselves as their own neighbors. 
Returns:
Error code:

Time complexity: O(V^2 d), V is the number of vertices in the vertex iterator given, d is the (maximum) degree of the vertices in the graph.
See also:

Example 13.22. File examples/simple/igraph_similarity.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %.2f", MATRIX(*m, i, j)); } fprintf(f, "\n"); } fprintf(f, "==========\n"); } int check_jaccard_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_jaccard(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_jaccard_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_jaccard_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int check_dice_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_dice(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_dice_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_dice_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int main() { igraph_t g; igraph_matrix_t m; int ret; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); ret = check_jaccard_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 1; } igraph_similarity_jaccard(&g, &m, igraph_vss_seq(1, 2), IGRAPH_ALL, 0); print_matrix(&m, stdout); ret = check_jaccard_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 3; } ret = check_jaccard_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 4; } ret = check_dice_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 5; } ret = check_dice_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 6; } ret = check_dice_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 7; } igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_ALL); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_OUT); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_IN); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
int igraph_similarity_dice_pairs(const igraph_t *graph, igraph_vector_t *res, const igraph_vector_t *pairs, igraph_neimode_t mode, igraph_bool_t loops);
The Dice similarity coefficient of two vertices is twice the number of common neighbors divided by the sum of the degrees of the vertices. This function calculates the pairwise Dice similarities for a list of vertex pairs.
Arguments:

The graph object to analyze 


Pointer to a vector, the result of the calculation will
be stored here. The number of elements is the same as the number
of pairs in 


A vector that contains the pairs for which the similarity will be calculated. Each pair is defined by two consecutive elements, i.e. the first and second element of the vector specifies the first pair, the third and fourth element specifies the second pair and so on. 


The type of neighbors to be used for the calculation in directed graphs. Possible values:



Whether to include the vertices themselves as their own neighbors. 
Returns:
Error code:

Time complexity: O(nd), n is the number of pairs in the given vector, d is the (maximum) degree of the vertices in the graph.
See also:

Example 13.23. File examples/simple/igraph_similarity.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %.2f", MATRIX(*m, i, j)); } fprintf(f, "\n"); } fprintf(f, "==========\n"); } int check_jaccard_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_jaccard(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_jaccard_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_jaccard_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int check_dice_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_dice(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_dice_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_dice_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int main() { igraph_t g; igraph_matrix_t m; int ret; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); ret = check_jaccard_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 1; } igraph_similarity_jaccard(&g, &m, igraph_vss_seq(1, 2), IGRAPH_ALL, 0); print_matrix(&m, stdout); ret = check_jaccard_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 3; } ret = check_jaccard_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 4; } ret = check_dice_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 5; } ret = check_dice_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 6; } ret = check_dice_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 7; } igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_ALL); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_OUT); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_IN); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
int igraph_similarity_dice_es(const igraph_t *graph, igraph_vector_t *res, const igraph_es_t es, igraph_neimode_t mode, igraph_bool_t loops);
The Dice similarity coefficient of two vertices is twice the number of common neighbors divided by the sum of the degrees of the vertices. This function calculates the pairwise Dice similarities for the endpoints of edges in a given edge selector.
Arguments:

The graph object to analyze 


Pointer to a vector, the result of the calculation will
be stored here. The number of elements is the same as the number
of edges in 


An edge selector that specifies the edges to be included in the result. 


The type of neighbors to be used for the calculation in directed graphs. Possible values:



Whether to include the vertices themselves as their own neighbors. 
Returns:
Error code:

Time complexity: O(nd), n is the number of pairs in the given vector, d is the (maximum) degree of the vertices in the graph.
See also:

Example 13.24. File examples/simple/igraph_similarity.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %.2f", MATRIX(*m, i, j)); } fprintf(f, "\n"); } fprintf(f, "==========\n"); } int check_jaccard_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_jaccard(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_jaccard_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_jaccard_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int check_dice_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_dice(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_dice_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_dice_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int main() { igraph_t g; igraph_matrix_t m; int ret; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); ret = check_jaccard_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 1; } igraph_similarity_jaccard(&g, &m, igraph_vss_seq(1, 2), IGRAPH_ALL, 0); print_matrix(&m, stdout); ret = check_jaccard_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 3; } ret = check_jaccard_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 4; } ret = check_dice_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 5; } ret = check_dice_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 6; } ret = check_dice_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 7; } igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_ALL); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_OUT); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_IN); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
int igraph_similarity_inverse_log_weighted(const igraph_t *graph, igraph_matrix_t *res, const igraph_vs_t vids, igraph_neimode_t mode);
The inverse logweighted similarity of two vertices is the number of their common neighbors, weighted by the inverse logarithm of their degrees. It is based on the assumption that two vertices should be considered more similar if they share a lowdegree common neighbor, since highdegree common neighbors are more likely to appear even by pure chance.
Isolated vertices will have zero similarity to any other vertex. Selfsimilarities are not calculated.
See the following paper for more details: Lada A. Adamic and Eytan Adar: Friends and neighbors on the Web. Social Networks, 25(3):211230, 2003.
Arguments:

The graph object to analyze. 


Pointer to a matrix, the result of the calculation will
be stored here. The number of its rows is the same as the
number of vertex ids in 


The vertex ids of the vertices for which the calculation will be done. 


The type of neighbors to be used for the calculation in directed graphs. Possible values:

Returns:
Error code:

Time complexity: O(Vd^2), V is the number of vertices in the graph, d is the (maximum) degree of the vertices in the graph.
Example 13.25. File examples/simple/igraph_similarity.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_matrix(igraph_matrix_t *m, FILE *f) { long int i, j; for (i = 0; i < igraph_matrix_nrow(m); i++) { for (j = 0; j < igraph_matrix_ncol(m); j++) { fprintf(f, " %.2f", MATRIX(*m, i, j)); } fprintf(f, "\n"); } fprintf(f, "==========\n"); } int check_jaccard_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_jaccard(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_jaccard_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_jaccard_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Jaccard similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int check_dice_all(const igraph_t* g, igraph_matrix_t* m, igraph_neimode_t mode, igraph_bool_t loops) { igraph_vector_t pairs, res; long int i, j, k, n; igraph_eit_t eit; igraph_vector_init(&res, 0); /* First, query the similarities for all the vertices to a matrix */ igraph_similarity_dice(g, m, igraph_vss_all(), mode, loops); /* Second, query the similarities for all pairs using a pair vector */ n = igraph_vcount(g); igraph_vector_init(&pairs, 0); for (i = 0; i < n; i++) { for (j = n  1; j >= 0; j) { igraph_vector_push_back(&pairs, i); igraph_vector_push_back(&pairs, j); } } igraph_similarity_dice_pairs(g, &res, &pairs, mode, loops); for (i = 0, k = 0; i < n; i++) { for (j = n  1; j >= 0; j, k++) { if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for vertex pair %ld%ld " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } } } igraph_vector_destroy(&pairs); /* Third, query the similarities for all edges */ igraph_similarity_dice_es(g, &res, igraph_ess_all(IGRAPH_EDGEORDER_FROM), mode, loops); igraph_eit_create(g, igraph_ess_all(IGRAPH_EDGEORDER_FROM), &eit); k = 0; while (!IGRAPH_EIT_END(eit)) { long int eid = IGRAPH_EIT_GET(eit); i = IGRAPH_FROM(g, eid); j = IGRAPH_TO(g, eid); if (fabs(VECTOR(res)[k]  MATRIX(*m, i, j)) > 1e6) { fprintf(stderr, "Dice similarity calculation for edge %ld%ld (ID=%ld) " "does not match the value in the full matrix (%.6f vs %.6f)\n", i, j, eid, VECTOR(res)[k], MATRIX(*m, i, j)); return 1; } IGRAPH_EIT_NEXT(eit); k++; } igraph_eit_destroy(&eit); igraph_vector_destroy(&res); return 0; } int main() { igraph_t g; igraph_matrix_t m; int ret; igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 0, 3, 0, 1); igraph_matrix_init(&m, 0, 0); ret = check_jaccard_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 1; } igraph_similarity_jaccard(&g, &m, igraph_vss_seq(1, 2), IGRAPH_ALL, 0); print_matrix(&m, stdout); ret = check_jaccard_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 3; } ret = check_jaccard_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 4; } ret = check_dice_all(&g, &m, IGRAPH_ALL, 1); print_matrix(&m, stdout); if (ret) { return 5; } ret = check_dice_all(&g, &m, IGRAPH_OUT, 1); print_matrix(&m, stdout); if (ret) { return 6; } ret = check_dice_all(&g, &m, IGRAPH_IN, 0); print_matrix(&m, stdout); if (ret) { return 7; } igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_ALL); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_OUT); print_matrix(&m, stdout); igraph_similarity_inverse_log_weighted(&g, &m, igraph_vss_all(), IGRAPH_IN); print_matrix(&m, stdout); igraph_matrix_destroy(&m); igraph_destroy(&g); return 0; }
igraph_minimum_spanning_tree
— Calculates one minimum spanning tree of a graph.igraph_minimum_spanning_tree_unweighted
— Calculates one minimum spanning tree of an unweighted graph.igraph_minimum_spanning_tree_prim
— Calculates one minimum spanning tree of a weighted graph.igraph_random_spanning_tree
— Uniformly sample the spanning trees of a graphigraph_is_tree
— Decides whether the graph is a tree.
int igraph_minimum_spanning_tree(const igraph_t* graph, igraph_vector_t* res, const igraph_vector_t* weights);
If the graph has more minimum spanning trees (this is always the case, except if it is a forest) this implementation returns only the same one.
Directed graphs are considered as undirected for this computation.
If the graph is not connected then its minimum spanning forest is returned. This is the set of the minimum spanning trees of each component.
Arguments:

The graph object. 

An initialized vector, the IDs of the edges that constitute
a spanning tree will be returned here. Use


A vector containing the weights of the edges in the same order as the simple edge iterator visits them (i.e. in increasing order of edge IDs). 
Returns:
Error code:

Time complexity: O(V+E) for the unweighted case, O(E log V) for the weighted case. V is the number of vertices, E the number of edges in the graph.
See also:

Example 13.26. File examples/simple/igraph_minimum_spanning_tree.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g, tree; igraph_vector_t eb, edges; long int i; igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 10, 0, 11, 0, 12, 0, 13, 0, 17, 0, 19, 0, 21, 0, 31, 1, 2, 1, 3, 1, 7, 1, 13, 1, 17, 1, 19, 1, 21, 1, 30, 2, 3, 2, 7, 2, 8, 2, 9, 2, 13, 2, 27, 2, 28, 2, 32, 3, 7, 3, 12, 3, 13, 4, 6, 4, 10, 5, 6, 5, 10, 5, 16, 6, 16, 8, 30, 8, 32, 8, 33, 9, 33, 13, 33, 14, 32, 14, 33, 15, 32, 15, 33, 18, 32, 18, 33, 19, 33, 20, 32, 20, 33, 22, 32, 22, 33, 23, 25, 23, 27, 23, 29, 23, 32, 23, 33, 24, 25, 24, 27, 24, 31, 25, 31, 26, 29, 26, 33, 27, 33, 28, 31, 28, 33, 29, 32, 29, 33, 30, 32, 30, 33, 31, 32, 31, 33, 32, 33, 1); igraph_vector_init(&eb, igraph_ecount(&g)); igraph_edge_betweenness(&g, &eb, IGRAPH_UNDIRECTED, /*weights=*/ 0); for (i = 0; i < igraph_vector_size(&eb); i++) { VECTOR(eb)[i] = VECTOR(eb)[i]; } igraph_minimum_spanning_tree_prim(&g, &tree, &eb); igraph_write_graph_edgelist(&tree, stdout); igraph_vector_init(&edges, 0); igraph_minimum_spanning_tree(&g, &edges, &eb); igraph_vector_print(&edges); igraph_vector_destroy(&edges); igraph_destroy(&tree); igraph_destroy(&g); igraph_vector_destroy(&eb); return 0; }
int igraph_minimum_spanning_tree_unweighted(const igraph_t *graph, igraph_t *mst);
If the graph has more minimum spanning trees (this is always the case, except if it is a forest) this implementation returns only the same one.
Directed graphs are considered as undirected for this computation.
If the graph is not connected then its minimum spanning forest is returned. This is the set of the minimum spanning trees of each component.
Arguments:

The graph object. 

The minimum spanning tree, another graph object. Do
not initialize this object before passing it to
this function, but be sure to call 
Returns:
Error code:

Time complexity: O(V+E), V is the number of vertices, E the number of edges in the graph.
See also:

int igraph_minimum_spanning_tree_prim(const igraph_t *graph, igraph_t *mst, const igraph_vector_t *weights);
This function uses Prim's method for carrying out the computation, see Prim, R.C.: Shortest connection networks and some generalizations, Bell System Technical Journal, Vol. 36, 1957, 13891401.
If the graph has more than one minimum spanning tree, the current implementation returns always the same one.
Directed graphs are considered as undirected for this computation.
If the graph is not connected then its minimum spanning forest is returned. This is the set of the minimum spanning trees of each component.
Arguments:

The graph object. 

The result of the computation, a graph object containing
the minimum spanning tree of the graph.
Do not initialize this object before passing it to
this function, but be sure to call 

A vector containing the weights of the edges in the same order as the simple edge iterator visits them (i.e. in increasing order of edge IDs). 
Returns:
Error code:

Time complexity: O(E log V), V is the number of vertices, E the number of edges in the graph.
See also:

Example 13.27. File examples/simple/igraph_minimum_spanning_tree.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g, tree; igraph_vector_t eb, edges; long int i; igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 10, 0, 11, 0, 12, 0, 13, 0, 17, 0, 19, 0, 21, 0, 31, 1, 2, 1, 3, 1, 7, 1, 13, 1, 17, 1, 19, 1, 21, 1, 30, 2, 3, 2, 7, 2, 8, 2, 9, 2, 13, 2, 27, 2, 28, 2, 32, 3, 7, 3, 12, 3, 13, 4, 6, 4, 10, 5, 6, 5, 10, 5, 16, 6, 16, 8, 30, 8, 32, 8, 33, 9, 33, 13, 33, 14, 32, 14, 33, 15, 32, 15, 33, 18, 32, 18, 33, 19, 33, 20, 32, 20, 33, 22, 32, 22, 33, 23, 25, 23, 27, 23, 29, 23, 32, 23, 33, 24, 25, 24, 27, 24, 31, 25, 31, 26, 29, 26, 33, 27, 33, 28, 31, 28, 33, 29, 32, 29, 33, 30, 32, 30, 33, 31, 32, 31, 33, 32, 33, 1); igraph_vector_init(&eb, igraph_ecount(&g)); igraph_edge_betweenness(&g, &eb, IGRAPH_UNDIRECTED, /*weights=*/ 0); for (i = 0; i < igraph_vector_size(&eb); i++) { VECTOR(eb)[i] = VECTOR(eb)[i]; } igraph_minimum_spanning_tree_prim(&g, &tree, &eb); igraph_write_graph_edgelist(&tree, stdout); igraph_vector_init(&edges, 0); igraph_minimum_spanning_tree(&g, &edges, &eb); igraph_vector_print(&edges); igraph_vector_destroy(&edges); igraph_destroy(&tree); igraph_destroy(&g); igraph_vector_destroy(&eb); return 0; }
int igraph_random_spanning_tree(const igraph_t *graph, igraph_vector_t *res, igraph_integer_t vid);
Performs a looperased random walk on the graph to uniformly sample its spanning trees. Edge directions are ignored.
Multigraphs are supported, and edge multiplicities will affect the sampling
frequency. For example, consider the 3cycle graph 1=231
, with two edges
between vertices 1 and 2. Due to these parallel edges, the trees 123
and 312
will be sampled with multiplicity 2, while the tree
231
will be sampled with multiplicity 1.
Arguments:

The input graph. Edge directions are ignored. 

An initialized vector, the IDs of the edges that constitute
a spanning tree will be returned here. Use


This parameter is relevant if the graph is not connected. If negative, a random spanning forest of all components will be generated. Otherwise, it should be the ID of a vertex. A random spanning tree of the component containing the vertex will be generated. 
Returns:
Error code. 
See also:
int igraph_is_tree(const igraph_t *graph, igraph_bool_t *res, igraph_integer_t *root, igraph_neimode_t mode);
An undirected graph is a tree if it is connected and has no cycles.
In the directed case, a possible additional requirement is that all
edges are oriented away from a root (outtree or arborescence) or all edges
are oriented towards a root (intree or antiarborescence).
This test can be controlled using the mode
parameter.
By convention, the null graph (i.e. the graph with no vertices) is considered not to be a tree.
Arguments:

The graph object to analyze. 

Pointer to a logical variable, the result will be stored here. 

If not 

For a directed graph this specifies whether to test for an
outtree, an intree or ignore edge directions. The respective
possible values are:

Returns:
Error code:

Time complexity: At most O(V+E), the number of vertices plus the number of edges in the graph.
See also:
igraph_is_weakly_connected() 
Example 13.28. File examples/simple/igraph_tree.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t graph; igraph_bool_t res; /* Create a directed binary tree on 15 nodes, with edges pointing towards the root. */ igraph_tree(&graph, 15, 2, IGRAPH_TREE_IN); igraph_is_tree(&graph, &res, NULL, IGRAPH_IN); printf("Is it an intree? %s\n", res ? "Yes" : "No"); igraph_is_tree(&graph, &res, NULL, IGRAPH_OUT); printf("Is it an outtree? %s\n", res ? "Yes" : "No"); igraph_destroy(&graph); return 0; }
igraph_transitivity_undirected
— Calculates the transitivity (clustering coefficient) of a graph.igraph_transitivity_local_undirected
— Calculates the local transitivity (clustering coefficient) of a graph.igraph_transitivity_avglocal_undirected
— Average local transitivity (clustering coefficient).igraph_transitivity_barrat
— Weighted transitivity, as defined by A. Barrat.
int igraph_transitivity_undirected(const igraph_t *graph, igraph_real_t *res, igraph_transitivity_mode_t mode);
The transitivity measures the probability that two neighbors of a vertex are connected. More precisely, this is the ratio of the triangles and connected triples in the graph, the result is a single real number. Directed graphs are considered as undirected ones.
Note that this measure is different from the local transitivity measure
(see igraph_transitivity_local_undirected()
) as it calculates a single
value for the whole graph. See the following reference for more details:
S. Wasserman and K. Faust: Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press, 1994.
Clustering coefficient is an alternative name for transitivity.
Arguments:

The graph object. 

Pointer to a real variable, the result will be stored here. 

Defines how to treat graphs with no connected triples.

Returns:
Error code:

See also:
Time complexity: O(V*d^2), V is the number of vertices in the graph, d is the average node degree.
Example 13.29. File examples/simple/igraph_transitivity.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g; igraph_real_t res; /* Trivial cases */ igraph_ring(&g, 100, IGRAPH_UNDIRECTED, 0, 0); igraph_transitivity_undirected(&g, &res, IGRAPH_TRANSITIVITY_NAN); igraph_destroy(&g); if (res != 0) { return 1; } igraph_full(&g, 20, IGRAPH_UNDIRECTED, IGRAPH_NO_LOOPS); igraph_transitivity_undirected(&g, &res, IGRAPH_TRANSITIVITY_NAN); igraph_destroy(&g); if (res != 1) { return 2; } /* Degenerate cases */ igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 1, 2, 3, 4, 5, 1); igraph_transitivity_undirected(&g, &res, IGRAPH_TRANSITIVITY_NAN); /* res should be NaN here, any comparison must return false */ if (res == 0  res > 0  res < 0) { return 4; } igraph_transitivity_undirected(&g, &res, IGRAPH_TRANSITIVITY_ZERO); /* res should be zero here */ if (res) { return 5; } igraph_destroy(&g); /* Zachary Karate club */ igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 10, 0, 11, 0, 12, 0, 13, 0, 17, 0, 19, 0, 21, 0, 31, 1, 2, 1, 3, 1, 7, 1, 13, 1, 17, 1, 19, 1, 21, 1, 30, 2, 3, 2, 7, 2, 8, 2, 9, 2, 13, 2, 27, 2, 28, 2, 32, 3, 7, 3, 12, 3, 13, 4, 6, 4, 10, 5, 6, 5, 10, 5, 16, 6, 16, 8, 30, 8, 32, 8, 33, 9, 33, 13, 33, 14, 32, 14, 33, 15, 32, 15, 33, 18, 32, 18, 33, 19, 33, 20, 32, 20, 33, 22, 32, 22, 33, 23, 25, 23, 27, 23, 29, 23, 32, 23, 33, 24, 25, 24, 27, 24, 31, 25, 31, 26, 29, 26, 33, 27, 33, 28, 31, 28, 33, 29, 32, 29, 33, 30, 32, 30, 33, 31, 32, 31, 33, 32, 33, 1); igraph_transitivity_undirected(&g, &res, IGRAPH_TRANSITIVITY_NAN); igraph_destroy(&g); if (res != 0.2556818181818181767717) { fprintf(stderr, "%f != %f\n", res, 0.2556818181818181767717); return 3; } return 0; }
int igraph_transitivity_local_undirected(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, igraph_transitivity_mode_t mode);
The transitivity measures the probability that two neighbors of a vertex are connected. In case of the local transitivity, this probability is calculated separately for each vertex.
Note that this measure is different from the global transitivity measure
(see igraph_transitivity_undirected()
) as it calculates a transitivity
value for each vertex individually. See the following reference for more
details:
D. J. Watts and S. Strogatz: Collective dynamics of smallworld networks. Nature 393(6684):440442 (1998).
Clustering coefficient is an alternative name for transitivity.
Arguments:

The input graph, which should be undirected and simple. 

Pointer to an initialized vector, the result will be stored here. It will be resized as needed. 

Vertex set, the vertices for which the local transitivity will be calculated. 

Defines how to treat vertices with degree less than two.

Returns:
Error code. 
See also:
Time complexity: O(n*d^2), n is the number of vertices for which the transitivity is calculated, d is the average vertex degree.
int igraph_transitivity_avglocal_undirected(const igraph_t *graph, igraph_real_t *res, igraph_transitivity_mode_t mode);
The transitivity measures the probability that two neighbors of a
vertex are connected. In case of the average local transitivity,
this probability is calculated for each vertex and then the average
is taken. Vertices with less than two neighbors require special treatment,
they will either be left out from the calculation or they will be considered
as having zero transitivity, depending on the mode
argument.
Note that this measure is different from the global transitivity measure
(see igraph_transitivity_undirected()
) as it simply takes the
average local transitivity across the whole network. See the following
reference for more details:
D. J. Watts and S. Strogatz: Collective dynamics of smallworld networks. Nature 393(6684):440442 (1998).
Clustering coefficient is an alternative name for transitivity.
Arguments:

The input graph, directed graphs are considered as undirected ones. 

Pointer to a real variable, the result will be stored here. 

Defines how to treat vertices with degree less than two.

Returns:
Error code. 
See also:
Time complexity: O(V*d^2), V is the number of vertices in the graph and d is the average degree.
int igraph_transitivity_barrat(const igraph_t *graph, igraph_vector_t *res, const igraph_vs_t vids, const igraph_vector_t *weights, igraph_transitivity_mode_t mode);
This is a local transitivity, i.e. a vertexlevel index. For a
given vertex i
, from all triangles in which it participates we
consider the weight of the edges incident on i
. The transitivity
is the sum of these weights divided by twice the strength of the
vertex (see igraph_strength()
) and the degree of the vertex
minus one. See Alain Barrat, Marc Barthelemy, Romualdo
PastorSatorras, Alessandro Vespignani: The architecture of complex
weighted networks, Proc. Natl. Acad. Sci. USA 101, 3747 (2004) at
http://arxiv.org/abs/condmat/0311416 for the exact formula.
Arguments:

The input graph, edge directions are ignored for directed graphs. Note that the function does NOT work for nonsimple graphs. 

Pointer to an initialized vector, the result will be stored here. It will be resized as needed. 

The vertices for which the calculation is performed. 

Edge weights. If this is a null pointer, then a
warning is given and 

Defines how to treat vertices with zero strength.

Returns:
Error code. 
Time complexity: O(V*d^2), V is the number of vertices in the graph, d is the average node degree.
See also:

int igraph_to_directed(igraph_t *graph, igraph_to_directed_t mode);
If the supplied graph is directed, this function does nothing.
Arguments:

The graph object to convert. 

Constant, specifies the details of how exactly the
conversion is done. Possible values: 
Returns:
Error code. 
Time complexity: O(V+E), the number of vertices plus the number of edges.
int igraph_to_undirected(igraph_t *graph, igraph_to_undirected_t mode, const igraph_attribute_combination_t *edge_comb);
If the supplied graph is undirected, this function does nothing.
Arguments:

The graph object to convert. 

Constant, specifies the details of how exactly the
conversion is done. Possible values: 

What to do with the edge attributes. See the igraph manual section about attributes for details. 
Returns:
Error code. 
Time complexity: O(V+E), the number of vertices plus the number of edges.
Example 13.30. File examples/simple/igraph_to_undirected.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_vector_t v; igraph_t g; igraph_vector_init_int(&v, 2, 5, 5); igraph_lattice(&g, &v, 1, IGRAPH_DIRECTED, 1 /*mutual*/, 0 /*circular*/); igraph_to_undirected(&g, IGRAPH_TO_UNDIRECTED_COLLAPSE, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_vector_destroy(&v); printf("\n"); igraph_small(&g, 10, IGRAPH_DIRECTED, 0, 1, 2, 1, 2, 3, 2, 3, 4, 3, 4, 3, 5, 6, 6, 5, 6, 7, 6, 7, 7, 6, 7, 8, 7, 8, 8, 7, 8, 7, 8, 8, 9, 9, 9, 9, 1); igraph_to_undirected(&g, IGRAPH_TO_UNDIRECTED_MUTUAL, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); return 0; }
int igraph_laplacian(const igraph_t *graph, igraph_matrix_t *res, igraph_sparsemat_t *sparseres, igraph_bool_t normalized, const igraph_vector_t *weights);
The graph Laplacian matrix is similar to an adjacency matrix but contains 1's instead of 1's and the vertex degrees are included in the diagonal. So the result for edge ij is 1 if i!=j and is equal to the degree of vertex i if i==j. igraph_laplacian will work on a directed graph; in this case, the diagonal will contain the outdegrees. Loop edges will be ignored.
The normalized version of the Laplacian matrix has 1 in the diagonal and 1/sqrt(d[i]d[j]) if there is an edge from i to j.
The first version of this function was written by Vincent Matossian.
Arguments:

Pointer to the graph to convert. 

Pointer to an initialized matrix object, the result is
stored here. It will be resized if needed.
If it is a null pointer, then it is ignored.
At least one of 

Pointer to an initialized sparse matrix object, the
result is stored here, if it is not a null pointer.
At least one of 

Whether to create a normalized Laplacian matrix. 

An optional vector containing edge weights, to calculate the weighted Laplacian matrix. Set it to a null pointer to calculate the unweighted Laplacian. 
Returns:
Error code. 
Time complexity: O(VV), V is the number of vertices in the graph.
Example 13.31. File examples/simple/igraph_laplacian.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> igraph_bool_t check_laplacian(igraph_t* graph, igraph_matrix_t* matrix, igraph_vector_t* w) { igraph_vector_t vec, res; long int i, j; igraph_vector_init(&vec, 0); igraph_vector_init(&res, igraph_vcount(graph)); if (w) { igraph_strength(graph, &vec, igraph_vss_all(), IGRAPH_OUT, IGRAPH_NO_LOOPS, w); } else { igraph_degree(graph, &vec, igraph_vss_all(), IGRAPH_OUT, IGRAPH_NO_LOOPS); } for (i = 0; i < igraph_vcount(graph); i++) { VECTOR(vec)[i] = sqrt(VECTOR(vec)[i]); } for (i = 0; i < igraph_vcount(graph); i++) { for (j = 0; j < igraph_vcount(graph); j++) { VECTOR(res)[i] += MATRIX(*matrix, i, j) * VECTOR(vec)[j]; } } if (igraph_vector_min(&res) > 1e7) { printf("Invalid Laplacian matrix:\n"); igraph_matrix_print(matrix); return 0; } igraph_vector_destroy(&vec); igraph_vector_destroy(&res); return 1; } int test_unnormalized_laplacian(igraph_vector_t* w, igraph_bool_t dir) { igraph_t g; igraph_matrix_t m, m2; igraph_sparsemat_t sm; igraph_vector_t vec, *weights = 0; igraph_matrix_init(&m, 1, 1); igraph_sparsemat_init(&sm, 0, 0, 0); if (w) { weights = (igraph_vector_t*)calloc(1, sizeof(igraph_vector_t)); igraph_vector_copy(weights, w); } /* No loop or multiple edges */ igraph_ring(&g, 5, dir, 0, 1); igraph_laplacian(&g, &m, &sm, 0, weights); igraph_matrix_init(&m2, 0, 0); igraph_sparsemat_as_matrix(&m2, &sm); if (!igraph_matrix_all_e_tol(&m, &m2, 0)) { return 41; } igraph_matrix_destroy(&m2); igraph_matrix_print(&m); printf("===\n"); /* Add some loop edges */ igraph_vector_init_real(&vec, 4, 1.0, 1.0, 2.0, 2.0); igraph_add_edges(&g, &vec, 0); igraph_vector_destroy(&vec); if (weights) { igraph_vector_push_back(weights, 2); igraph_vector_push_back(weights, 2); } igraph_laplacian(&g, &m, &sm, 0, weights); igraph_matrix_init(&m2, 0, 0); igraph_sparsemat_as_matrix(&m2, &sm); if (!igraph_matrix_all_e_tol(&m, &m2, 0)) { return 42; } igraph_matrix_destroy(&m2); igraph_matrix_print(&m); printf("===\n"); /* Duplicate some edges */ igraph_vector_init_real(&vec, 4, 1.0, 2.0, 3.0, 4.0); igraph_add_edges(&g, &vec, 0); igraph_vector_destroy(&vec); if (weights) { igraph_vector_push_back(weights, 3); igraph_vector_push_back(weights, 3); } igraph_laplacian(&g, &m, &sm, 0, weights); igraph_matrix_init(&m2, 0, 0); igraph_sparsemat_as_matrix(&m2, &sm); if (!igraph_matrix_all_e_tol(&m, &m2, 0)) { return 43; } igraph_matrix_destroy(&m2); igraph_matrix_print(&m); igraph_destroy(&g); igraph_matrix_destroy(&m); if (weights) { igraph_vector_destroy(weights); free(weights); } igraph_sparsemat_destroy(&sm); return 0; } int test_normalized_laplacian(igraph_vector_t *w, igraph_bool_t dir) { igraph_t g; igraph_matrix_t m, m2; igraph_sparsemat_t sm; igraph_vector_t vec, *weights = 0; igraph_bool_t ok = 1; igraph_matrix_init(&m, 1, 1); igraph_sparsemat_init(&sm, 0, 0, 0); if (w) { weights = (igraph_vector_t*)calloc(1, sizeof(igraph_vector_t)); igraph_vector_copy(weights, w); } /* Undirected graph, no loop or multiple edges */ igraph_ring(&g, 5, dir, 0, 1); igraph_laplacian(&g, &m, &sm, 1, weights); igraph_matrix_init(&m2, 0, 0); igraph_sparsemat_as_matrix(&m2, &sm); if (!igraph_matrix_all_e_tol(&m, &m2, 0)) { return 44; } igraph_matrix_destroy(&m2); ok = ok && check_laplacian(&g, &m, weights); /* Add some loop edges */ igraph_vector_init_real(&vec, 4, 1.0, 1.0, 2.0, 2.0); igraph_add_edges(&g, &vec, 0); igraph_vector_destroy(&vec); if (weights) { igraph_vector_push_back(weights, 2); igraph_vector_push_back(weights, 2); } igraph_laplacian(&g, &m, &sm, 1, weights); igraph_matrix_init(&m2, 0, 0); igraph_sparsemat_as_matrix(&m2, &sm); if (!igraph_matrix_all_e_tol(&m, &m2, 0)) { return 45; } igraph_matrix_destroy(&m2); ok = ok && check_laplacian(&g, &m, weights); /* Duplicate some edges */ igraph_vector_init_real(&vec, 4, 1.0, 2.0, 3.0, 4.0); igraph_add_edges(&g, &vec, 0); igraph_vector_destroy(&vec); if (weights) { igraph_vector_push_back(weights, 3); igraph_vector_push_back(weights, 3); } igraph_laplacian(&g, &m, &sm, 1, weights); igraph_matrix_init(&m2, 0, 0); igraph_sparsemat_as_matrix(&m2, &sm); if (!igraph_matrix_all_e_tol(&m, &m2, 0)) { return 46; } igraph_matrix_destroy(&m2); ok = ok && check_laplacian(&g, &m, weights); igraph_destroy(&g); igraph_matrix_destroy(&m); if (weights) { igraph_vector_destroy(weights); free(weights); } if (ok) { printf("OK\n"); } igraph_sparsemat_destroy(&sm); return !ok; } int main() { int res; int i; igraph_vector_t weights; igraph_vector_init_real(&weights, 5, 1.0, 2.0, 3.0, 4.0, 5.0); for (i = 0; i < 8; i++) { igraph_bool_t is_normalized = i / 4; igraph_vector_t* v = ((i & 2) / 2 ? &weights : 0); igraph_bool_t dir = (i % 2 ? IGRAPH_DIRECTED : IGRAPH_UNDIRECTED); printf("=== %sormalized, %sweighted, %sdirected\n", (is_normalized ? "N" : "Unn"), (v != 0 ? "" : "un"), (dir == IGRAPH_DIRECTED ? "" : "un") ); if (is_normalized) { res = test_normalized_laplacian(v, dir); } else { res = test_unnormalized_laplacian(v, dir); } if (res) { return i + 1; } } igraph_vector_destroy(&weights); return 0; }
igraph_is_simple
— Decides whether the input graph is a simple graph.igraph_is_loop
— Find the loop edges in a graph.igraph_is_multiple
— Find the multiple edges in a graph.igraph_has_multiple
— Check whether the graph has at least one multiple edge.igraph_count_multiple
— Count the number of appearances of the edges in a graph.igraph_simplify
— Removes loop and/or multiple edges from the graph.
int igraph_is_simple(const igraph_t *graph, igraph_bool_t *res);
A graph is a simple graph if it does not contain loop edges and multiple edges.
Arguments:

The input graph. 

Pointer to a boolean constant, the result is stored here. 
Returns:
Error code. 
See also:

Time complexity: O(V+E).
int igraph_is_loop(const igraph_t *graph, igraph_vector_bool_t *res, igraph_es_t es);
A loop edge is an edge from a vertex to itself.
Arguments:

The input graph. 

Pointer to an initialized boolean vector for storing the result, it will be resized as needed. 

The edges to check, for all edges supply 
Returns:
Error code. 
See also:

Time complexity: O(e), the number of edges to check.
Example 13.32. File examples/simple/igraph_is_loop.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20072012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_vector(igraph_vector_bool_t *v, FILE *f) { long int i; for (i = 0; i < igraph_vector_bool_size(v); i++) { fprintf(f, " %i", (int) VECTOR(*v)[i]); } fprintf(f, "\n"); } int main() { igraph_t graph; igraph_vector_bool_t v; igraph_vector_bool_init(&v, 0); igraph_small(&graph, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 1, 0, 1, 1, 0, 3, 4, 11, 10, 1); igraph_is_loop(&graph, &v, igraph_ess_all(IGRAPH_EDGEORDER_ID)); print_vector(&v, stdout); igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_UNDIRECTED, 0, 0, 1, 1, 2, 2, 2, 3, 2, 4, 2, 5, 2, 6, 2, 2, 0, 0, 1); igraph_is_loop(&graph, &v, igraph_ess_all(IGRAPH_EDGEORDER_ID)); print_vector(&v, stdout); igraph_destroy(&graph); igraph_vector_bool_destroy(&v); return 0; }
int igraph_is_multiple(const igraph_t *graph, igraph_vector_bool_t *res, igraph_es_t es);
An edge is a multiple edge if there is another edge with the same head and tail vertices in the graph.
Note that this function returns true only for the second or more appearances of the multiple edges.
Arguments:

The input graph. 

Pointer to a boolean vector, the result will be stored here. It will be resized as needed. 

The edges to check. Supply 
Returns:
Error code. 
See also:
Time complexity: O(e*d), e is the number of edges to check and d is the average degree (outdegree in directed graphs) of the vertices at the tail of the edges.
Example 13.33. File examples/simple/igraph_is_multiple.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20072012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_vector(igraph_vector_bool_t *v, FILE *f) { long int i; for (i = 0; i < igraph_vector_bool_size(v); i++) { fprintf(f, " %i", (int) VECTOR(*v)[i]); } fprintf(f, "\n"); } int main() { igraph_t graph; igraph_vector_bool_t v; igraph_vector_bool_init(&v, 0); igraph_small(&graph, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 1, 0, 1, 1, 0, 3, 4, 11, 10, 1); igraph_is_multiple(&graph, &v, igraph_ess_all(IGRAPH_EDGEORDER_ID)); print_vector(&v, stdout); igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_UNDIRECTED, 0, 0, 1, 2, 1, 1, 2, 2, 2, 1, 2, 3, 2, 4, 2, 5, 2, 6, 2, 2, 3, 2, 0, 0, 6, 2, 2, 2, 0, 0, 1); igraph_is_multiple(&graph, &v, igraph_ess_all(IGRAPH_EDGEORDER_ID)); print_vector(&v, stdout); igraph_destroy(&graph); igraph_vector_bool_destroy(&v); return 0; }
int igraph_has_multiple(const igraph_t *graph, igraph_bool_t *res);
An edge is a multiple edge if there is another edge with the same head and tail vertices in the graph.
Arguments:

The input graph. 

Pointer to a boolean variable, the result will be stored here. 
Returns:
Error code. 
See also:
Time complexity: O(e*d), e is the number of edges to check and d is the average degree (outdegree in directed graphs) of the vertices at the tail of the edges.
Example 13.34. File examples/simple/igraph_has_multiple.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20072012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_vector(igraph_vector_bool_t *v, FILE *f) { long int i; for (i = 0; i < igraph_vector_bool_size(v); i++) { fprintf(f, " %i", (int) VECTOR(*v)[i]); } fprintf(f, "\n"); } int main() { igraph_t graph; igraph_bool_t res; igraph_small(&graph, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 1, 0, 1, 1, 0, 3, 4, 11, 10, 1); igraph_has_multiple(&graph, &res); if (!res) { return 1; } igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_UNDIRECTED, 0, 0, 1, 2, 1, 1, 2, 2, 2, 1, 2, 3, 2, 4, 2, 5, 2, 6, 2, 2, 3, 2, 0, 0, 6, 2, 2, 2, 0, 0, 1); igraph_has_multiple(&graph, &res); if (!res) { return 2; } igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 1, 1, 0, 3, 4, 11, 10, 1); igraph_has_multiple(&graph, &res); if (res) { return 3; } igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_UNDIRECTED, 0, 0, 1, 2, 1, 1, 2, 2, 2, 3, 2, 4, 2, 5, 2, 6, 2, 2, 1); igraph_has_multiple(&graph, &res); if (!res) { return 4; } igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_UNDIRECTED, 0, 0, 1, 2, 1, 1, 2, 2, 2, 3, 2, 4, 2, 5, 2, 6, 1); igraph_has_multiple(&graph, &res); if (res) { return 5; } igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_UNDIRECTED, 0, 1, 0, 1, 1, 2, 1); igraph_has_multiple(&graph, &res); if (!res) { return 6; } igraph_destroy(&graph); igraph_small(&graph, 0, IGRAPH_UNDIRECTED, 0, 0, 0, 0, 1); igraph_has_multiple(&graph, &res); if (!res) { return 7; } igraph_destroy(&graph); return 0; }
int igraph_count_multiple(const igraph_t *graph, igraph_vector_t *res, igraph_es_t es);
If the graph has no multiple edges then the result vector will be filled with ones. (An edge is a multiple edge if there is another edge with the same head and tail vertices in the graph.)
Arguments:

The input graph. 

Pointer to a vector, the result will be stored here. It will be resized as needed. 

The edges to check. Supply 
Returns:
Error code. 
See also:
Time complexity: O(e*d), e is the number of edges to check and d is the average degree (outdegree in directed graphs) of the vertices at the tail of the edges.
int igraph_simplify(igraph_t *graph, igraph_bool_t multiple, igraph_bool_t loops, const igraph_attribute_combination_t *edge_comb);
Arguments:

The graph object. 

Logical, if true, multiple edges will be removed. 

Logical, if true, loops (self edges) will be removed. 

What to do with the edge attributes. See the igraph manual section about attributes for details. 
Returns:
Error code:

Time complexity: O(V+E).
Example 13.35. File examples/simple/igraph_simplify.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> int main() { igraph_t g; /* Multiple edges */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1); igraph_simplify(&g, 1, 1, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_UNDIRECTED, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1); igraph_simplify(&g, 1, 1, /*edge_comb=*/ 0); if (igraph_ecount(&g) != 1) { return 1; } igraph_destroy(&g); /* Loop edges*/ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 0, 1, 1, 2, 2, 1, 2, 1); igraph_simplify(&g, 1, 1, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_UNDIRECTED, 0, 0, 1, 1, 2, 2, 1, 2, 1); igraph_simplify(&g, 1, 1, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); /* Loop & multiple edges */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1); igraph_simplify(&g, 1 /* multiple */, 0 /* loop */, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_UNDIRECTED, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1); igraph_simplify(&g, 1 /* multiple */, 0 /* loop */, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_DIRECTED, 2, 2, 2, 2, 2, 2, 3, 2, 1); igraph_simplify(&g, 0 /* multiple */, 1 /* loop */, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_UNDIRECTED, 3, 3, 3, 3, 3, 4, 1); igraph_simplify(&g, 0 /* multiple */, 1 /* loop */, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_DIRECTED, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 1); igraph_simplify(&g, 1, 1, /*edge_comb=*/ 0); igraph_write_graph_edgelist(&g, stdout); igraph_destroy(&g); igraph_small(&g, 0, IGRAPH_UNDIRECTED, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 3, 2, 3, 2, 1); igraph_simplify(&g, 1, 1, /*edge_comb=*/ 0); if (igraph_ecount(&g) != 1) { return 2; } igraph_destroy(&g); return 0; }
int igraph_assortativity_nominal(const igraph_t *graph, const igraph_vector_t *types, igraph_real_t *res, igraph_bool_t directed);
Assuming the vertices of the input graph belong to different categories, this function calculates the assortativity coefficient of the graph. The assortativity coefficient is between minus one and one and it is one if all connections stay within categories, it is minus one, if the network is perfectly disassortative. For a randomly connected network it is (asymptotically) zero.
See equation (2) in M. E. J. Newman: Mixing patterns in networks, Phys. Rev. E 67, 026126 (2003) (http://arxiv.org/abs/condmat/0209450) for the proper definition.
Arguments:

The input graph, it can be directed or undirected. 

Vector giving the vertex types. They are assumed to be integer numbers, starting with zero. 

Pointer to a real variable, the result is stored here. 

Boolean, it gives whether to consider edge directions in a directed graph. It is ignored for undirected graphs. 
Returns:
Error code. 
Time complexity: O(E+t), E is the number of edges, t is the number of vertex types.
See also:

Example 13.36. File examples/simple/assortativity.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20092012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdio.h> int main() { igraph_t g; FILE *karate, *neural; igraph_real_t res; igraph_vector_t types; igraph_vector_t degree, outdegree, indegree; igraph_real_t football_types[] = { 7, 0, 2, 3, 7, 3, 2, 8, 8, 7, 3, 10, 6, 2, 6, 2, 7, 9, 6, 1, 9, 8, 8, 7, 10, 0, 6, 9, 11, 1, 1, 6, 2, 0, 6, 1, 5, 0, 6, 2, 3, 7, 5, 6, 4, 0, 11, 2, 4, 11, 10, 8, 3, 11, 6, 1, 9, 4, 11, 10, 2, 6, 9, 10, 2, 9, 4, 11, 8, 10, 9, 6, 3, 11, 3, 4, 9, 8, 8, 1, 5, 3, 5, 11, 3, 6, 4, 9, 11, 0, 5, 4, 4, 7, 1, 9, 9, 10, 3, 6, 2, 1, 3, 0, 7, 0, 2, 3, 8, 0, 4, 8, 4, 9, 11 }; karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_vector_init(&types, 0); igraph_degree(&g, &types, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); /**/ neural = fopen("celegansneural.gml", "r"); igraph_read_graph_gml(&g, neural); fclose(neural); igraph_degree(&g, &types, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); igraph_vector_destroy(&types); /**/ karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_vector_init(°ree, 0); igraph_degree(&g, °ree, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_vector_add_constant(°ree, 1); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); /**/ neural = fopen("celegansneural.gml", "r"); igraph_read_graph_gml(&g, neural); fclose(neural); igraph_degree(&g, °ree, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_vector_add_constant(°ree, 1); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_vector_destroy(°ree); /**/ igraph_vector_init(&indegree, 0); igraph_vector_init(&outdegree, 0); igraph_degree(&g, &indegree, igraph_vss_all(), IGRAPH_IN, /*loops=*/ 1); igraph_degree(&g, &outdegree, igraph_vss_all(), IGRAPH_OUT, /*loops=*/ 1); igraph_vector_add_constant(&indegree, 1); igraph_vector_add_constant(&outdegree, 1); igraph_assortativity(&g, &outdegree, &indegree, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_vector_destroy(&indegree); igraph_vector_destroy(&outdegree); /**/ igraph_assortativity_degree(&g, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_destroy(&g); /**/ karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_assortativity_degree(&g, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_destroy(&g); /**/ igraph_small(&g, sizeof(football_types) / sizeof(igraph_real_t), IGRAPH_UNDIRECTED, 0, 1, 2, 3, 0, 4, 4, 5, 3, 5, 2, 6, 6, 7, 7, 8, 8, 9, 0, 9, 4, 9, 5, 10, 10, 11, 5, 11, 3, 11, 12, 13, 2, 13, 2, 14, 12, 14, 14, 15, 13, 15, 2, 15, 4, 16, 9, 16, 0, 16, 16, 17, 12, 17, 12, 18, 18, 19, 17, 20, 20, 21, 8, 21, 7, 21, 9, 22, 7, 22, 21, 22, 8, 22, 22, 23, 9, 23, 4, 23, 16, 23, 0, 23, 11, 24, 24, 25, 1, 25, 3, 26, 12, 26, 14, 26, 26, 27, 17, 27, 1, 27, 17, 27, 4, 28, 11, 28, 24, 28, 19, 29, 29, 30, 19, 30, 18, 31, 31, 32, 21, 32, 15, 32, 13, 32, 6, 32, 0, 33, 1, 33, 25, 33, 19, 33, 31, 34, 26, 34, 12, 34, 18, 34, 34, 35, 0, 35, 29, 35, 19, 35, 30, 35, 18, 36, 12, 36, 20, 36, 19, 36, 36, 37, 1, 37, 25, 37, 33, 37, 18, 38, 16, 38, 28, 38, 26, 38, 14, 38, 12, 38, 38, 39, 6, 39, 32, 39, 13, 39, 15, 39, 7, 40, 3, 40, 40, 41, 8, 41, 4, 41, 23, 41, 9, 41, 0, 41, 16, 41, 34, 42, 29, 42, 18, 42, 26, 42, 42, 43, 36, 43, 26, 43, 31, 43, 38, 43, 12, 43, 14, 43, 19, 44, 35, 44, 30, 44, 44, 45, 13, 45, 33, 45, 1, 45, 37, 45, 25, 45, 21, 46, 46, 47, 22, 47, 6, 47, 15, 47, 2, 47, 39, 47, 32, 47, 44, 48, 48, 49, 32, 49, 46, 49, 30, 50, 24, 50, 11, 50, 28, 50, 50, 51, 40, 51, 8, 51, 22, 51, 21, 51, 3, 52, 40, 52, 5, 52, 52, 53, 25, 53, 48, 53, 49, 53, 46, 53, 39, 54, 31, 54, 38, 54, 14, 54, 34, 54, 18, 54, 54, 55, 31, 55, 6, 55, 35, 55, 29, 55, 19, 55, 30, 55, 27, 56, 56, 57, 1, 57, 42, 57, 44, 57, 48, 57, 3, 58, 6, 58, 17, 58, 36, 58, 36, 59, 58, 59, 59, 60, 10, 60, 39, 60, 6, 60, 47, 60, 13, 60, 15, 60, 2, 60, 43, 61, 47, 61, 54, 61, 18, 61, 26, 61, 31, 61, 34, 61, 61, 62, 20, 62, 45, 62, 17, 62, 27, 62, 56, 62, 27, 63, 58, 63, 59, 63, 42, 63, 63, 64, 9, 64, 32, 64, 60, 64, 2, 64, 6, 64, 47, 64, 13, 64, 0, 65, 27, 65, 17, 65, 63, 65, 56, 65, 20, 65, 65, 66, 59, 66, 24, 66, 44, 66, 48, 66, 16, 67, 41, 67, 46, 67, 53, 67, 49, 67, 67, 68, 15, 68, 50, 68, 21, 68, 51, 68, 7, 68, 22, 68, 8, 68, 4, 69, 24, 69, 28, 69, 50, 69, 11, 69, 69, 70, 43, 70, 65, 70, 20, 70, 56, 70, 62, 70, 27, 70, 60, 71, 18, 71, 14, 71, 34, 71, 54, 71, 38, 71, 61, 71, 31, 71, 71, 72, 2, 72, 10, 72, 3, 72, 40, 72, 52, 72, 7, 73, 49, 73, 53, 73, 67, 73, 46, 73, 73, 74, 2, 74, 72, 74, 5, 74, 10, 74, 52, 74, 3, 74, 40, 74, 20, 75, 66, 75, 48, 75, 57, 75, 44, 75, 75, 76, 27, 76, 59, 76, 20, 76, 70, 76, 66, 76, 56, 76, 62, 76, 73, 77, 22, 77, 7, 77, 51, 77, 21, 77, 8, 77, 77, 78, 23, 78, 50, 78, 28, 78, 22, 78, 8, 78, 68, 78, 7, 78, 51, 78, 31, 79, 43, 79, 30, 79, 19, 79, 29, 79, 35, 79, 55, 79, 79, 80, 37, 80, 29, 80, 16, 81, 5, 81, 40, 81, 10, 81, 72, 81, 3, 81, 81, 82, 74, 82, 39, 82, 77, 82, 80, 82, 30, 82, 29, 82, 7, 82, 53, 83, 81, 83, 69, 83, 73, 83, 46, 83, 67, 83, 49, 83, 83, 84, 24, 84, 49, 84, 52, 84, 3, 84, 74, 84, 10, 84, 81, 84, 5, 84, 3, 84, 6, 85, 14, 85, 38, 85, 43, 85, 80, 85, 12, 85, 26, 85, 31, 85, 44, 86, 53, 86, 75, 86, 57, 86, 48, 86, 80, 86, 66, 86, 86, 87, 17, 87, 62, 87, 56, 87, 24, 87, 20, 87, 65, 87, 49, 88, 58, 88, 83, 88, 69, 88, 46, 88, 53, 88, 73, 88, 67, 88, 88, 89, 1, 89, 37, 89, 25, 89, 33, 89, 55, 89, 45, 89, 5, 90, 8, 90, 23, 90, 0, 90, 11, 90, 50, 90, 24, 90, 69, 90, 28, 90, 29, 91, 48, 91, 66, 91, 69, 91, 44, 91, 86, 91, 57, 91, 80, 91, 91, 92, 35, 92, 15, 92, 86, 92, 48, 92, 57, 92, 61, 92, 66, 92, 75, 92, 0, 93, 23, 93, 80, 93, 16, 93, 4, 93, 82, 93, 91, 93, 41, 93, 9, 93, 34, 94, 19, 94, 55, 94, 79, 94, 80, 94, 29, 94, 30, 94, 82, 94, 35, 94, 70, 95, 69, 95, 76, 95, 62, 95, 56, 95, 27, 95, 17, 95, 87, 95, 37, 95, 48, 96, 17, 96, 76, 96, 27, 96, 56, 96, 65, 96, 20, 96, 87, 96, 5, 97, 86, 97, 58, 97, 11, 97, 59, 97, 63, 97, 97, 98, 77, 98, 48, 98, 84, 98, 40, 98, 10, 98, 5, 98, 52, 98, 81, 98, 89, 99, 34, 99, 14, 99, 85, 99, 54, 99, 18, 99, 31, 99, 61, 99, 71, 99, 14, 99, 99, 100, 82, 100, 13, 100, 2, 100, 15, 100, 32, 100, 64, 100, 47, 100, 39, 100, 6, 100, 51, 101, 30, 101, 94, 101, 1, 101, 79, 101, 58, 101, 19, 101, 55, 101, 35, 101, 29, 101, 100, 102, 74, 102, 52, 102, 98, 102, 72, 102, 40, 102, 10, 102, 3, 102, 102, 103, 33, 103, 45, 103, 25, 103, 89, 103, 37, 103, 1, 103, 70, 103, 72, 104, 11, 104, 0, 104, 93, 104, 67, 104, 41, 104, 16, 104, 87, 104, 23, 104, 4, 104, 9, 104, 89, 105, 103, 105, 33, 105, 62, 105, 37, 105, 45, 105, 1, 105, 80, 105, 25, 105, 25, 106, 56, 106, 92, 106, 2, 106, 13, 106, 32, 106, 60, 106, 6, 106, 64, 106, 15, 106, 39, 106, 88, 107, 75, 107, 98, 107, 102, 107, 72, 107, 40, 107, 81, 107, 5, 107, 10, 107, 84, 107, 4, 108, 9, 108, 7, 108, 51, 108, 77, 108, 21, 108, 78, 108, 22, 108, 68, 108, 79, 109, 30, 109, 63, 109, 1, 109, 33, 109, 103, 109, 105, 109, 45, 109, 25, 109, 89, 109, 37, 109, 67, 110, 13, 110, 24, 110, 80, 110, 88, 110, 49, 110, 73, 110, 46, 110, 83, 110, 53, 110, 23, 111, 64, 111, 46, 111, 78, 111, 8, 111, 21, 111, 51, 111, 7, 111, 108, 111, 68, 111, 77, 111, 52, 112, 96, 112, 97, 112, 57, 112, 66, 112, 63, 112, 44, 112, 92, 112, 75, 112, 91, 112, 28, 113, 20, 113, 95, 113, 59, 113, 70, 113, 17, 113, 87, 113, 76, 113, 65, 113, 96, 113, 83, 114, 88, 114, 110, 114, 53, 114, 49, 114, 73, 114, 46, 114, 67, 114, 58, 114, 15, 114, 104, 114, 1); igraph_simplify(&g, /*multiple=*/ 1, /*loops=*/ 1, /*edge_comb=*/ 0); igraph_vector_view(&types, football_types, sizeof(football_types) / sizeof(igraph_real_t)); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); return 0; }
int igraph_assortativity(const igraph_t *graph, const igraph_vector_t *types1, const igraph_vector_t *types2, igraph_real_t *res, igraph_bool_t directed);
This function calculates the assortativity coefficient of the input graph. This coefficient is basically the correlation between the actual connectivity patterns of the vertices and the pattern expected from the distribution of the vertex types.
See equation (21) in M. E. J. Newman: Mixing patterns in networks, Phys. Rev. E 67, 026126 (2003) (http://arxiv.org/abs/condmat/0209450) for the proper definition. The actual calculation is performed using equation (26) in the same paper for directed graphs, and equation (4) in M. E. J. Newman: Assortative mixing in networks, Phys. Rev. Lett. 89, 208701 (2002) (http://arxiv.org/abs/condmat/0205405/) for undirected graphs.
Arguments:

The input graph, it can be directed or undirected. 

The vertex values, these can be arbitrary numeric values. 

A second value vector to be using for the incoming edges when calculating assortativity for a directed graph. Supply a null pointer here if you want to use the same values for outgoing and incoming edges. This argument is ignored (with a warning) if it is not a null pointer and undirected assortativity coefficient is being calculated. 

Pointer to a real variable, the result is stored here. 

Boolean, whether to consider edge directions for directed graphs. It is ignored for undirected graphs. 
Returns:
Error code. 
Time complexity: O(E), linear in the number of edges of the graph.
See also:

Example 13.37. File examples/simple/assortativity.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20092012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdio.h> int main() { igraph_t g; FILE *karate, *neural; igraph_real_t res; igraph_vector_t types; igraph_vector_t degree, outdegree, indegree; igraph_real_t football_types[] = { 7, 0, 2, 3, 7, 3, 2, 8, 8, 7, 3, 10, 6, 2, 6, 2, 7, 9, 6, 1, 9, 8, 8, 7, 10, 0, 6, 9, 11, 1, 1, 6, 2, 0, 6, 1, 5, 0, 6, 2, 3, 7, 5, 6, 4, 0, 11, 2, 4, 11, 10, 8, 3, 11, 6, 1, 9, 4, 11, 10, 2, 6, 9, 10, 2, 9, 4, 11, 8, 10, 9, 6, 3, 11, 3, 4, 9, 8, 8, 1, 5, 3, 5, 11, 3, 6, 4, 9, 11, 0, 5, 4, 4, 7, 1, 9, 9, 10, 3, 6, 2, 1, 3, 0, 7, 0, 2, 3, 8, 0, 4, 8, 4, 9, 11 }; karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_vector_init(&types, 0); igraph_degree(&g, &types, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); /**/ neural = fopen("celegansneural.gml", "r"); igraph_read_graph_gml(&g, neural); fclose(neural); igraph_degree(&g, &types, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); igraph_vector_destroy(&types); /**/ karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_vector_init(°ree, 0); igraph_degree(&g, °ree, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_vector_add_constant(°ree, 1); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); /**/ neural = fopen("celegansneural.gml", "r"); igraph_read_graph_gml(&g, neural); fclose(neural); igraph_degree(&g, °ree, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_vector_add_constant(°ree, 1); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_vector_destroy(°ree); /**/ igraph_vector_init(&indegree, 0); igraph_vector_init(&outdegree, 0); igraph_degree(&g, &indegree, igraph_vss_all(), IGRAPH_IN, /*loops=*/ 1); igraph_degree(&g, &outdegree, igraph_vss_all(), IGRAPH_OUT, /*loops=*/ 1); igraph_vector_add_constant(&indegree, 1); igraph_vector_add_constant(&outdegree, 1); igraph_assortativity(&g, &outdegree, &indegree, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_vector_destroy(&indegree); igraph_vector_destroy(&outdegree); /**/ igraph_assortativity_degree(&g, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_destroy(&g); /**/ karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_assortativity_degree(&g, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_destroy(&g); /**/ igraph_small(&g, sizeof(football_types) / sizeof(igraph_real_t), IGRAPH_UNDIRECTED, 0, 1, 2, 3, 0, 4, 4, 5, 3, 5, 2, 6, 6, 7, 7, 8, 8, 9, 0, 9, 4, 9, 5, 10, 10, 11, 5, 11, 3, 11, 12, 13, 2, 13, 2, 14, 12, 14, 14, 15, 13, 15, 2, 15, 4, 16, 9, 16, 0, 16, 16, 17, 12, 17, 12, 18, 18, 19, 17, 20, 20, 21, 8, 21, 7, 21, 9, 22, 7, 22, 21, 22, 8, 22, 22, 23, 9, 23, 4, 23, 16, 23, 0, 23, 11, 24, 24, 25, 1, 25, 3, 26, 12, 26, 14, 26, 26, 27, 17, 27, 1, 27, 17, 27, 4, 28, 11, 28, 24, 28, 19, 29, 29, 30, 19, 30, 18, 31, 31, 32, 21, 32, 15, 32, 13, 32, 6, 32, 0, 33, 1, 33, 25, 33, 19, 33, 31, 34, 26, 34, 12, 34, 18, 34, 34, 35, 0, 35, 29, 35, 19, 35, 30, 35, 18, 36, 12, 36, 20, 36, 19, 36, 36, 37, 1, 37, 25, 37, 33, 37, 18, 38, 16, 38, 28, 38, 26, 38, 14, 38, 12, 38, 38, 39, 6, 39, 32, 39, 13, 39, 15, 39, 7, 40, 3, 40, 40, 41, 8, 41, 4, 41, 23, 41, 9, 41, 0, 41, 16, 41, 34, 42, 29, 42, 18, 42, 26, 42, 42, 43, 36, 43, 26, 43, 31, 43, 38, 43, 12, 43, 14, 43, 19, 44, 35, 44, 30, 44, 44, 45, 13, 45, 33, 45, 1, 45, 37, 45, 25, 45, 21, 46, 46, 47, 22, 47, 6, 47, 15, 47, 2, 47, 39, 47, 32, 47, 44, 48, 48, 49, 32, 49, 46, 49, 30, 50, 24, 50, 11, 50, 28, 50, 50, 51, 40, 51, 8, 51, 22, 51, 21, 51, 3, 52, 40, 52, 5, 52, 52, 53, 25, 53, 48, 53, 49, 53, 46, 53, 39, 54, 31, 54, 38, 54, 14, 54, 34, 54, 18, 54, 54, 55, 31, 55, 6, 55, 35, 55, 29, 55, 19, 55, 30, 55, 27, 56, 56, 57, 1, 57, 42, 57, 44, 57, 48, 57, 3, 58, 6, 58, 17, 58, 36, 58, 36, 59, 58, 59, 59, 60, 10, 60, 39, 60, 6, 60, 47, 60, 13, 60, 15, 60, 2, 60, 43, 61, 47, 61, 54, 61, 18, 61, 26, 61, 31, 61, 34, 61, 61, 62, 20, 62, 45, 62, 17, 62, 27, 62, 56, 62, 27, 63, 58, 63, 59, 63, 42, 63, 63, 64, 9, 64, 32, 64, 60, 64, 2, 64, 6, 64, 47, 64, 13, 64, 0, 65, 27, 65, 17, 65, 63, 65, 56, 65, 20, 65, 65, 66, 59, 66, 24, 66, 44, 66, 48, 66, 16, 67, 41, 67, 46, 67, 53, 67, 49, 67, 67, 68, 15, 68, 50, 68, 21, 68, 51, 68, 7, 68, 22, 68, 8, 68, 4, 69, 24, 69, 28, 69, 50, 69, 11, 69, 69, 70, 43, 70, 65, 70, 20, 70, 56, 70, 62, 70, 27, 70, 60, 71, 18, 71, 14, 71, 34, 71, 54, 71, 38, 71, 61, 71, 31, 71, 71, 72, 2, 72, 10, 72, 3, 72, 40, 72, 52, 72, 7, 73, 49, 73, 53, 73, 67, 73, 46, 73, 73, 74, 2, 74, 72, 74, 5, 74, 10, 74, 52, 74, 3, 74, 40, 74, 20, 75, 66, 75, 48, 75, 57, 75, 44, 75, 75, 76, 27, 76, 59, 76, 20, 76, 70, 76, 66, 76, 56, 76, 62, 76, 73, 77, 22, 77, 7, 77, 51, 77, 21, 77, 8, 77, 77, 78, 23, 78, 50, 78, 28, 78, 22, 78, 8, 78, 68, 78, 7, 78, 51, 78, 31, 79, 43, 79, 30, 79, 19, 79, 29, 79, 35, 79, 55, 79, 79, 80, 37, 80, 29, 80, 16, 81, 5, 81, 40, 81, 10, 81, 72, 81, 3, 81, 81, 82, 74, 82, 39, 82, 77, 82, 80, 82, 30, 82, 29, 82, 7, 82, 53, 83, 81, 83, 69, 83, 73, 83, 46, 83, 67, 83, 49, 83, 83, 84, 24, 84, 49, 84, 52, 84, 3, 84, 74, 84, 10, 84, 81, 84, 5, 84, 3, 84, 6, 85, 14, 85, 38, 85, 43, 85, 80, 85, 12, 85, 26, 85, 31, 85, 44, 86, 53, 86, 75, 86, 57, 86, 48, 86, 80, 86, 66, 86, 86, 87, 17, 87, 62, 87, 56, 87, 24, 87, 20, 87, 65, 87, 49, 88, 58, 88, 83, 88, 69, 88, 46, 88, 53, 88, 73, 88, 67, 88, 88, 89, 1, 89, 37, 89, 25, 89, 33, 89, 55, 89, 45, 89, 5, 90, 8, 90, 23, 90, 0, 90, 11, 90, 50, 90, 24, 90, 69, 90, 28, 90, 29, 91, 48, 91, 66, 91, 69, 91, 44, 91, 86, 91, 57, 91, 80, 91, 91, 92, 35, 92, 15, 92, 86, 92, 48, 92, 57, 92, 61, 92, 66, 92, 75, 92, 0, 93, 23, 93, 80, 93, 16, 93, 4, 93, 82, 93, 91, 93, 41, 93, 9, 93, 34, 94, 19, 94, 55, 94, 79, 94, 80, 94, 29, 94, 30, 94, 82, 94, 35, 94, 70, 95, 69, 95, 76, 95, 62, 95, 56, 95, 27, 95, 17, 95, 87, 95, 37, 95, 48, 96, 17, 96, 76, 96, 27, 96, 56, 96, 65, 96, 20, 96, 87, 96, 5, 97, 86, 97, 58, 97, 11, 97, 59, 97, 63, 97, 97, 98, 77, 98, 48, 98, 84, 98, 40, 98, 10, 98, 5, 98, 52, 98, 81, 98, 89, 99, 34, 99, 14, 99, 85, 99, 54, 99, 18, 99, 31, 99, 61, 99, 71, 99, 14, 99, 99, 100, 82, 100, 13, 100, 2, 100, 15, 100, 32, 100, 64, 100, 47, 100, 39, 100, 6, 100, 51, 101, 30, 101, 94, 101, 1, 101, 79, 101, 58, 101, 19, 101, 55, 101, 35, 101, 29, 101, 100, 102, 74, 102, 52, 102, 98, 102, 72, 102, 40, 102, 10, 102, 3, 102, 102, 103, 33, 103, 45, 103, 25, 103, 89, 103, 37, 103, 1, 103, 70, 103, 72, 104, 11, 104, 0, 104, 93, 104, 67, 104, 41, 104, 16, 104, 87, 104, 23, 104, 4, 104, 9, 104, 89, 105, 103, 105, 33, 105, 62, 105, 37, 105, 45, 105, 1, 105, 80, 105, 25, 105, 25, 106, 56, 106, 92, 106, 2, 106, 13, 106, 32, 106, 60, 106, 6, 106, 64, 106, 15, 106, 39, 106, 88, 107, 75, 107, 98, 107, 102, 107, 72, 107, 40, 107, 81, 107, 5, 107, 10, 107, 84, 107, 4, 108, 9, 108, 7, 108, 51, 108, 77, 108, 21, 108, 78, 108, 22, 108, 68, 108, 79, 109, 30, 109, 63, 109, 1, 109, 33, 109, 103, 109, 105, 109, 45, 109, 25, 109, 89, 109, 37, 109, 67, 110, 13, 110, 24, 110, 80, 110, 88, 110, 49, 110, 73, 110, 46, 110, 83, 110, 53, 110, 23, 111, 64, 111, 46, 111, 78, 111, 8, 111, 21, 111, 51, 111, 7, 111, 108, 111, 68, 111, 77, 111, 52, 112, 96, 112, 97, 112, 57, 112, 66, 112, 63, 112, 44, 112, 92, 112, 75, 112, 91, 112, 28, 113, 20, 113, 95, 113, 59, 113, 70, 113, 17, 113, 87, 113, 76, 113, 65, 113, 96, 113, 83, 114, 88, 114, 110, 114, 53, 114, 49, 114, 73, 114, 46, 114, 67, 114, 58, 114, 15, 114, 104, 114, 1); igraph_simplify(&g, /*multiple=*/ 1, /*loops=*/ 1, /*edge_comb=*/ 0); igraph_vector_view(&types, football_types, sizeof(football_types) / sizeof(igraph_real_t)); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); return 0; }
int igraph_assortativity_degree(const igraph_t *graph, igraph_real_t *res, igraph_bool_t directed);
Assortativity based on vertex degree, please see the discussion at
the documentation of igraph_assortativity()
for details.
Arguments:

The input graph, it can be directed or undirected. 

Pointer to a real variable, the result is stored here. 

Boolean, whether to consider edge directions for directed graphs. This argument is ignored for undirected graphs. Supply 1 (=TRUE) here to do the natural thing, i.e. use directed version of the measure for directed graphs and the undirected version for undirected graphs. 
Returns:
Error code. 
Time complexity: O(E+V), E is the number of edges, V is the number of vertices.
See also:

Example 13.38. File examples/simple/assortativity.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20092012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard street, Cambridge, MA 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <stdio.h> int main() { igraph_t g; FILE *karate, *neural; igraph_real_t res; igraph_vector_t types; igraph_vector_t degree, outdegree, indegree; igraph_real_t football_types[] = { 7, 0, 2, 3, 7, 3, 2, 8, 8, 7, 3, 10, 6, 2, 6, 2, 7, 9, 6, 1, 9, 8, 8, 7, 10, 0, 6, 9, 11, 1, 1, 6, 2, 0, 6, 1, 5, 0, 6, 2, 3, 7, 5, 6, 4, 0, 11, 2, 4, 11, 10, 8, 3, 11, 6, 1, 9, 4, 11, 10, 2, 6, 9, 10, 2, 9, 4, 11, 8, 10, 9, 6, 3, 11, 3, 4, 9, 8, 8, 1, 5, 3, 5, 11, 3, 6, 4, 9, 11, 0, 5, 4, 4, 7, 1, 9, 9, 10, 3, 6, 2, 1, 3, 0, 7, 0, 2, 3, 8, 0, 4, 8, 4, 9, 11 }; karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_vector_init(&types, 0); igraph_degree(&g, &types, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); /**/ neural = fopen("celegansneural.gml", "r"); igraph_read_graph_gml(&g, neural); fclose(neural); igraph_degree(&g, &types, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); igraph_vector_destroy(&types); /**/ karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_vector_init(°ree, 0); igraph_degree(&g, °ree, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_vector_add_constant(°ree, 1); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); /**/ neural = fopen("celegansneural.gml", "r"); igraph_read_graph_gml(&g, neural); fclose(neural); igraph_degree(&g, °ree, igraph_vss_all(), IGRAPH_ALL, /*loops=*/ 1); igraph_vector_add_constant(°ree, 1); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_assortativity(&g, °ree, 0, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_vector_destroy(°ree); /**/ igraph_vector_init(&indegree, 0); igraph_vector_init(&outdegree, 0); igraph_degree(&g, &indegree, igraph_vss_all(), IGRAPH_IN, /*loops=*/ 1); igraph_degree(&g, &outdegree, igraph_vss_all(), IGRAPH_OUT, /*loops=*/ 1); igraph_vector_add_constant(&indegree, 1); igraph_vector_add_constant(&outdegree, 1); igraph_assortativity(&g, &outdegree, &indegree, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_vector_destroy(&indegree); igraph_vector_destroy(&outdegree); /**/ igraph_assortativity_degree(&g, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_destroy(&g); /**/ karate = fopen("karate.gml", "r"); igraph_read_graph_gml(&g, karate); fclose(karate); igraph_assortativity_degree(&g, &res, /*directed=*/ 1); printf("%.5f\n", res); igraph_destroy(&g); /**/ igraph_small(&g, sizeof(football_types) / sizeof(igraph_real_t), IGRAPH_UNDIRECTED, 0, 1, 2, 3, 0, 4, 4, 5, 3, 5, 2, 6, 6, 7, 7, 8, 8, 9, 0, 9, 4, 9, 5, 10, 10, 11, 5, 11, 3, 11, 12, 13, 2, 13, 2, 14, 12, 14, 14, 15, 13, 15, 2, 15, 4, 16, 9, 16, 0, 16, 16, 17, 12, 17, 12, 18, 18, 19, 17, 20, 20, 21, 8, 21, 7, 21, 9, 22, 7, 22, 21, 22, 8, 22, 22, 23, 9, 23, 4, 23, 16, 23, 0, 23, 11, 24, 24, 25, 1, 25, 3, 26, 12, 26, 14, 26, 26, 27, 17, 27, 1, 27, 17, 27, 4, 28, 11, 28, 24, 28, 19, 29, 29, 30, 19, 30, 18, 31, 31, 32, 21, 32, 15, 32, 13, 32, 6, 32, 0, 33, 1, 33, 25, 33, 19, 33, 31, 34, 26, 34, 12, 34, 18, 34, 34, 35, 0, 35, 29, 35, 19, 35, 30, 35, 18, 36, 12, 36, 20, 36, 19, 36, 36, 37, 1, 37, 25, 37, 33, 37, 18, 38, 16, 38, 28, 38, 26, 38, 14, 38, 12, 38, 38, 39, 6, 39, 32, 39, 13, 39, 15, 39, 7, 40, 3, 40, 40, 41, 8, 41, 4, 41, 23, 41, 9, 41, 0, 41, 16, 41, 34, 42, 29, 42, 18, 42, 26, 42, 42, 43, 36, 43, 26, 43, 31, 43, 38, 43, 12, 43, 14, 43, 19, 44, 35, 44, 30, 44, 44, 45, 13, 45, 33, 45, 1, 45, 37, 45, 25, 45, 21, 46, 46, 47, 22, 47, 6, 47, 15, 47, 2, 47, 39, 47, 32, 47, 44, 48, 48, 49, 32, 49, 46, 49, 30, 50, 24, 50, 11, 50, 28, 50, 50, 51, 40, 51, 8, 51, 22, 51, 21, 51, 3, 52, 40, 52, 5, 52, 52, 53, 25, 53, 48, 53, 49, 53, 46, 53, 39, 54, 31, 54, 38, 54, 14, 54, 34, 54, 18, 54, 54, 55, 31, 55, 6, 55, 35, 55, 29, 55, 19, 55, 30, 55, 27, 56, 56, 57, 1, 57, 42, 57, 44, 57, 48, 57, 3, 58, 6, 58, 17, 58, 36, 58, 36, 59, 58, 59, 59, 60, 10, 60, 39, 60, 6, 60, 47, 60, 13, 60, 15, 60, 2, 60, 43, 61, 47, 61, 54, 61, 18, 61, 26, 61, 31, 61, 34, 61, 61, 62, 20, 62, 45, 62, 17, 62, 27, 62, 56, 62, 27, 63, 58, 63, 59, 63, 42, 63, 63, 64, 9, 64, 32, 64, 60, 64, 2, 64, 6, 64, 47, 64, 13, 64, 0, 65, 27, 65, 17, 65, 63, 65, 56, 65, 20, 65, 65, 66, 59, 66, 24, 66, 44, 66, 48, 66, 16, 67, 41, 67, 46, 67, 53, 67, 49, 67, 67, 68, 15, 68, 50, 68, 21, 68, 51, 68, 7, 68, 22, 68, 8, 68, 4, 69, 24, 69, 28, 69, 50, 69, 11, 69, 69, 70, 43, 70, 65, 70, 20, 70, 56, 70, 62, 70, 27, 70, 60, 71, 18, 71, 14, 71, 34, 71, 54, 71, 38, 71, 61, 71, 31, 71, 71, 72, 2, 72, 10, 72, 3, 72, 40, 72, 52, 72, 7, 73, 49, 73, 53, 73, 67, 73, 46, 73, 73, 74, 2, 74, 72, 74, 5, 74, 10, 74, 52, 74, 3, 74, 40, 74, 20, 75, 66, 75, 48, 75, 57, 75, 44, 75, 75, 76, 27, 76, 59, 76, 20, 76, 70, 76, 66, 76, 56, 76, 62, 76, 73, 77, 22, 77, 7, 77, 51, 77, 21, 77, 8, 77, 77, 78, 23, 78, 50, 78, 28, 78, 22, 78, 8, 78, 68, 78, 7, 78, 51, 78, 31, 79, 43, 79, 30, 79, 19, 79, 29, 79, 35, 79, 55, 79, 79, 80, 37, 80, 29, 80, 16, 81, 5, 81, 40, 81, 10, 81, 72, 81, 3, 81, 81, 82, 74, 82, 39, 82, 77, 82, 80, 82, 30, 82, 29, 82, 7, 82, 53, 83, 81, 83, 69, 83, 73, 83, 46, 83, 67, 83, 49, 83, 83, 84, 24, 84, 49, 84, 52, 84, 3, 84, 74, 84, 10, 84, 81, 84, 5, 84, 3, 84, 6, 85, 14, 85, 38, 85, 43, 85, 80, 85, 12, 85, 26, 85, 31, 85, 44, 86, 53, 86, 75, 86, 57, 86, 48, 86, 80, 86, 66, 86, 86, 87, 17, 87, 62, 87, 56, 87, 24, 87, 20, 87, 65, 87, 49, 88, 58, 88, 83, 88, 69, 88, 46, 88, 53, 88, 73, 88, 67, 88, 88, 89, 1, 89, 37, 89, 25, 89, 33, 89, 55, 89, 45, 89, 5, 90, 8, 90, 23, 90, 0, 90, 11, 90, 50, 90, 24, 90, 69, 90, 28, 90, 29, 91, 48, 91, 66, 91, 69, 91, 44, 91, 86, 91, 57, 91, 80, 91, 91, 92, 35, 92, 15, 92, 86, 92, 48, 92, 57, 92, 61, 92, 66, 92, 75, 92, 0, 93, 23, 93, 80, 93, 16, 93, 4, 93, 82, 93, 91, 93, 41, 93, 9, 93, 34, 94, 19, 94, 55, 94, 79, 94, 80, 94, 29, 94, 30, 94, 82, 94, 35, 94, 70, 95, 69, 95, 76, 95, 62, 95, 56, 95, 27, 95, 17, 95, 87, 95, 37, 95, 48, 96, 17, 96, 76, 96, 27, 96, 56, 96, 65, 96, 20, 96, 87, 96, 5, 97, 86, 97, 58, 97, 11, 97, 59, 97, 63, 97, 97, 98, 77, 98, 48, 98, 84, 98, 40, 98, 10, 98, 5, 98, 52, 98, 81, 98, 89, 99, 34, 99, 14, 99, 85, 99, 54, 99, 18, 99, 31, 99, 61, 99, 71, 99, 14, 99, 99, 100, 82, 100, 13, 100, 2, 100, 15, 100, 32, 100, 64, 100, 47, 100, 39, 100, 6, 100, 51, 101, 30, 101, 94, 101, 1, 101, 79, 101, 58, 101, 19, 101, 55, 101, 35, 101, 29, 101, 100, 102, 74, 102, 52, 102, 98, 102, 72, 102, 40, 102, 10, 102, 3, 102, 102, 103, 33, 103, 45, 103, 25, 103, 89, 103, 37, 103, 1, 103, 70, 103, 72, 104, 11, 104, 0, 104, 93, 104, 67, 104, 41, 104, 16, 104, 87, 104, 23, 104, 4, 104, 9, 104, 89, 105, 103, 105, 33, 105, 62, 105, 37, 105, 45, 105, 1, 105, 80, 105, 25, 105, 25, 106, 56, 106, 92, 106, 2, 106, 13, 106, 32, 106, 60, 106, 6, 106, 64, 106, 15, 106, 39, 106, 88, 107, 75, 107, 98, 107, 102, 107, 72, 107, 40, 107, 81, 107, 5, 107, 10, 107, 84, 107, 4, 108, 9, 108, 7, 108, 51, 108, 77, 108, 21, 108, 78, 108, 22, 108, 68, 108, 79, 109, 30, 109, 63, 109, 1, 109, 33, 109, 103, 109, 105, 109, 45, 109, 25, 109, 89, 109, 37, 109, 67, 110, 13, 110, 24, 110, 80, 110, 88, 110, 49, 110, 73, 110, 46, 110, 83, 110, 53, 110, 23, 111, 64, 111, 46, 111, 78, 111, 8, 111, 21, 111, 51, 111, 7, 111, 108, 111, 68, 111, 77, 111, 52, 112, 96, 112, 97, 112, 57, 112, 66, 112, 63, 112, 44, 112, 92, 112, 75, 112, 91, 112, 28, 113, 20, 113, 95, 113, 59, 113, 70, 113, 17, 113, 87, 113, 76, 113, 65, 113, 96, 113, 83, 114, 88, 114, 110, 114, 53, 114, 49, 114, 73, 114, 46, 114, 67, 114, 58, 114, 15, 114, 104, 114, 1); igraph_simplify(&g, /*multiple=*/ 1, /*loops=*/ 1, /*edge_comb=*/ 0); igraph_vector_view(&types, football_types, sizeof(football_types) / sizeof(igraph_real_t)); igraph_assortativity_nominal(&g, &types, &res, /*directed=*/ 0); printf("%.5f\n", res); igraph_destroy(&g); return 0; }
int igraph_coreness(const igraph_t *graph, igraph_vector_t *cores, igraph_neimode_t mode);
The kcore of a graph is a maximal subgraph in which each vertex has at least degree k. (Degree here means the degree in the subgraph of course.). The coreness of a vertex is the highest order of a kcore containing the vertex.
This function implements the algorithm presented in Vladimir Batagelj, Matjaz Zaversnik: An O(m) Algorithm for Cores Decomposition of Networks.
Arguments:

The input graph. 

Pointer to an initialized vector, the result of the computation will be stored here. It will be resized as needed. For each vertex it contains the highest order of a core containing the vertex. 

For directed graph it specifies whether to calculate
incores, outcores or the undirected version. It is ignored
for undirected graphs. Possible values: 
Returns:
Error code. 
Time complexity: O(E), the number of edges.
int igraph_is_dag(const igraph_t* graph, igraph_bool_t *res);
A directed acyclic graph (DAG) is a directed graph with no cycles.
Arguments:

The input graph. 

Pointer to a boolean constant, the result is stored here. 
Returns:
Error code. 
Time complexity: O(V+E), where V and E are the number of vertices and edges in the original input graph.
See also:

int igraph_topological_sorting(const igraph_t* graph, igraph_vector_t *res, igraph_neimode_t mode);
A topological sorting of a directed acyclic graph is a linear ordering of its nodes where each node comes before all nodes to which it has edges. Every DAG has at least one topological sort, and may have many. This function returns a possible topological sort among them. If the graph is not acyclic (it has at least one cycle), a partial topological sort is returned and a warning is issued.
Arguments:

The input graph. 

Pointer to a vector, the result will be stored here. It will be resized if needed. 

Specifies how to use the direction of the edges.
For 
Returns:
Error code. 
Time complexity: O(V+E), where V and E are the number of vertices and edges in the original input graph.
See also:

Example 13.39. File examples/simple/igraph_topological_sorting.c
/* * mode: C * */ /* IGraph library. Copyright (C) 20062012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> void print_vector(igraph_vector_t *v, FILE *f) { long int i; for (i = 0; i < igraph_vector_size(v); i++) { fprintf(f, " %d", (int)VECTOR(*v)[i]); } fprintf(f, "\n"); } void igraph_warning_handler_print_stdout(const char *reason, const char *file, int line, int igraph_errno) { fprintf(stdout, "Warning: %s\n", reason); } int main() { igraph_t g; igraph_vector_t v, res; igraph_bool_t is_dag; int ret; igraph_set_warning_handler(igraph_warning_handler_print_stdout); /* Test graph taken from http://en.wikipedia.org/wiki/Topological_sorting * @ 05.03.2006 */ igraph_small(&g, 8, 1, 0, 3, 0, 4, 1, 3, 2, 4, 2, 7, \ 3, 5, 3, 6, 3, 7, 4, 6, 1); igraph_vector_init(&res, 0); igraph_is_dag(&g, &is_dag); if (!is_dag) { return 2; } igraph_topological_sorting(&g, &res, IGRAPH_OUT); print_vector(&res, stdout); igraph_topological_sorting(&g, &res, IGRAPH_IN); print_vector(&res, stdout); /* Add a circle: 5 > 0 */ igraph_vector_init_int(&v, 2, 5, 0); igraph_add_edges(&g, &v, 0); igraph_is_dag(&g, &is_dag); if (is_dag) { return 3; } igraph_topological_sorting(&g, &res, IGRAPH_OUT); print_vector(&res, stdout); igraph_vector_destroy(&v); igraph_destroy(&g); /* Error handling */ igraph_set_error_handler(igraph_error_handler_ignore); /* This graph is the same but undirected */ igraph_small(&g, 8, 0, 0, 3, 0, 4, 1, 3, 2, 4, 2, 7, \ 3, 5, 3, 6, 3, 7, 4, 6, 1); igraph_is_dag(&g, &is_dag); if (is_dag) { return 4; } ret = igraph_topological_sorting(&g, &res, IGRAPH_ALL); if (ret != IGRAPH_EINVAL) { return 1; } ret = igraph_topological_sorting(&g, &res, IGRAPH_OUT); if (ret != IGRAPH_EINVAL) { return 1; } igraph_destroy(&g); igraph_vector_destroy(&res); return 0; }
int igraph_feedback_arc_set(const igraph_t *graph, igraph_vector_t *result, const igraph_vector_t *weights, igraph_fas_algorithm_t algo);
algorithms.
A feedback arc set is a set of edges whose removal makes the graph acyclic. We are usually interested in minimum feedback arc sets, i.e. sets of edges whose total weight is minimal among all the feedback arc sets.
For undirected graphs, the problem is simple: one has to find a maximum weight spanning tree and then remove all the edges not in the spanning tree. For directed graphs, this is an NPhard problem, and various heuristics are usually used to find an approximate solution to the problem. This function implements a few of these heuristics.
Arguments:

The graph object. 


An initialized vector, the result will be returned here. 


Weight vector or NULL if no weights are specified. 


The algorithm to use to solve the problem if the graph is directed. Possible values:

Returns:
Error code:

Example 13.40. File examples/simple/igraph_feedback_arc_set.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20112012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <string.h> int main() { igraph_t g; igraph_vector_t weights, result; igraph_bool_t dag; igraph_vector_init(&result, 0); /***********************************************************************/ /* Approximation with Eades' method */ /***********************************************************************/ /* Simple unweighted graph */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 0, 2, 3, 2, 4, 0, 4, 4, 3, 5, 0, 6, 5, 1); igraph_feedback_arc_set(&g, &result, 0, IGRAPH_FAS_APPROX_EADES); igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 1; } igraph_destroy(&g); /* Simple weighted graph */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 0, 2, 3, 2, 4, 0, 4, 4, 3, 5, 0, 6, 5, 1); igraph_vector_init_int_end(&weights, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1); igraph_feedback_arc_set(&g, &result, &weights, IGRAPH_FAS_APPROX_EADES); igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 2; } igraph_vector_destroy(&weights); igraph_destroy(&g); /* Simple unweighted graph with loops */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 0, 2, 3, 2, 4, 0, 4, 4, 3, 5, 0, 6, 5, 1, 1, 4, 4, 1); igraph_feedback_arc_set(&g, &result, 0, IGRAPH_FAS_APPROX_EADES); igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 3; } igraph_destroy(&g); igraph_vector_destroy(&result); return 0; }
Example 13.41. File examples/simple/igraph_feedback_arc_set_ip.c
/* * mode: C * */ /* vim:set ts=4 sw=4 sts=4 et: */ /* IGraph library. Copyright (C) 20112012 Gabor Csardi <csardi.gabor@gmail.com> 334 Harvard st, Cambridge MA, 02139 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA */ #include <igraph.h> #include <string.h> int main() { igraph_t g; igraph_vector_t weights, result; igraph_bool_t dag; int retval; igraph_vector_init(&result, 0); igraph_set_error_handler(&igraph_error_handler_printignore); /***********************************************************************/ /* Exact solution with integer programming */ /***********************************************************************/ /* Simple unweighted graph */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 0, 2, 3, 2, 4, 0, 4, 4, 3, 5, 0, 6, 5, 1); retval = igraph_feedback_arc_set(&g, &result, 0, IGRAPH_FAS_EXACT_IP); if (retval == IGRAPH_UNIMPLEMENTED) { return 77; } igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 1; } igraph_destroy(&g); /* Simple weighted graph */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 0, 2, 3, 2, 4, 0, 4, 4, 3, 5, 0, 6, 5, 1); igraph_vector_init_int_end(&weights, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1); igraph_feedback_arc_set(&g, &result, &weights, IGRAPH_FAS_EXACT_IP); igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 2; } igraph_vector_destroy(&weights); igraph_destroy(&g); /* Simple unweighted graph with loops */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 0, 2, 3, 2, 4, 0, 4, 4, 3, 5, 0, 6, 5, 1, 1, 4, 4, 1); igraph_feedback_arc_set(&g, &result, 0, IGRAPH_FAS_EXACT_IP); igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 3; } igraph_destroy(&g); /* Disjoint union of two almost identical graphs */ igraph_small(&g, 0, IGRAPH_DIRECTED, 0, 1, 1, 2, 2, 0, 2, 3, 2, 4, 0, 4, 4, 3, 5, 0, 6, 5, 1, 1, 4, 4, 7, 8, 8, 9, 9, 7, 9, 10, 9, 11, 7, 11, 11, 10, 12, 7, 13, 12, 1); igraph_feedback_arc_set(&g, &result, 0, IGRAPH_FAS_EXACT_IP); igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 4; } igraph_destroy(&g); /* Graph with lots of isolated vertices */ igraph_small(&g, 10000, IGRAPH_DIRECTED, 0, 1, 1); igraph_feedback_arc_set(&g, &result, 0, IGRAPH_FAS_EXACT_IP); igraph_vector_print(&result); igraph_delete_edges(&g, igraph_ess_vector(&result)); igraph_is_dag(&g, &dag); if (!dag) { return 5; } igraph_destroy(&g); igraph_vector_destroy(&result); return 0; }
Time complexity: depends on algo
, see the time complexities there.
int igraph_maximum_cardinality_search(const igraph_t *graph, igraph_vector_t *alpha, igraph_vector_t *alpham1);
This function implements the maximum cardinality search algorithm discussed in Robert E Tarjan and Mihalis Yannakakis: Simple lineartime algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs. SIAM Journal of Computation 13, 566579, 1984.
Arguments:

The input graph, which should be undirected and simple. of the edges is ignored. 

Pointer to an initialized vector, the result is stored here. It will be resized, as needed. Upon return it contains the rank of the each vertex. 

Pointer to an initialized vector or a 
Returns:
Error code. 
Time complexity: O(V+E), linear in terms of the number of vertices and edges.
See also:
int igraph_is_chordal(const igraph_t *graph, const igraph_vector_t *alpha, const igraph_vector_t *alpham1, igraph_bool_t *chordal, igraph_vector_t *fill_in, igraph_t *newgraph);
A graph is chordal if each of its cycles of four or more nodes
has a chord, which is an edge joining two nodes that are not
adjacent in the cycle. An equivalent definition is that any
chordless cycles have at most three nodes.
If either alpha
or alpha1
is given, then the other is
calculated by taking simply the inverse. If neither are given,
then igraph_maximum_cardinality_search()
is called to calculate
them.
Arguments:

The input graph, it might be directed, but edge direction is ignored. 

Either an alpha vector coming from


Either an inverse alpha vector coming from 

Pointer to a boolean, the result is stored here. 

Pointer to an initialized vector, or a null pointer. If not a null pointer, then the fillin of the graph is stored here. The fillin is the set of edges that are needed to make the graph chordal. The vector is resized as needed. 

Pointer to an uninitialized graph, or a null pointer. If not a null pointer, then a new triangulated graph is created here. This essentially means adding the fillin edges to the original graph. 
Returns:
