Use this if you are using igraph from R
Query and manipulate a graph as it were an adjacency matrix
## S3 method for class 'igraph' x[ i, j, ..., from, to, sparse = igraph_opt("sparsematrices"), edges = FALSE, drop = TRUE, attr = if (is_weighted(x)) "weight" else NULL ]
x |
The graph. |
i |
Index. Vertex ids or names or logical vectors. See details below. |
j |
Index. Vertex ids or names or logical vectors. See details below. |
... |
Currently ignored. |
from |
A numeric or character vector giving vertex ids or
names. Together with the |
to |
A numeric or character vector giving vertex ids or
names. Together with the |
sparse |
Logical scalar, whether to return sparse matrices. |
edges |
Logical scalar, whether to return edge ids. |
drop |
Ignored. |
attr |
If not |
The single bracket indexes the (possibly weighted) adjacency matrix of the graph. Here is what you can do with it:
Check whether there is an edge between two vertices (v and w) in the graph:
graph[v, w]
A numeric scalar is returned, one if the edge exists, zero otherwise.
Extract the (sparse) adjacency matrix of the graph, or part of it:
graph[] graph[1:3,5:6] graph[c(1,3,5),]
The first variants returns the full adjacency matrix, the other two return part of it.
The from
and to
arguments can be used to check
the existence of many edges. In this case, both from
and
to
must be present and they must have the same length. They
must contain vertex ids or names. A numeric vector is returned, of
the same length as from
and to
, it contains ones
for existing edges edges and zeros for non-existing ones.
Example:
graph[from=1:3, to=c(2,3,5)]
.
For weighted graphs, the [
operator returns the edge
weights. For non-esistent edges zero weights are returned. Other
edge attributes can be queried as well, by giving the attr
argument.
Querying edge ids instead of the existance of edges or edge attributes. E.g.
graph[1, 2, edges=TRUE]
returns the id of the edge between vertices 1 and 2, or zero if there is no such edge.
Adding one or more edges to a graph. For this the element(s) of
the imaginary adjacency matrix must be set to a non-zero numeric
value (or TRUE
):
graph[1, 2] <- 1 graph[1:3,1] <- 1 graph[from=1:3, to=c(2,3,5)] <- TRUE
This does not affect edges that are already present in the graph, i.e. no multiple edges are created.
Adding weighted edges to a graph. The attr
argument
contains the name of the edge attribute to set, so it does not
have to be ‘weight’:
graph[1, 2, attr="weight"]<- 5 graph[from=1:3, to=c(2,3,5)] <- c(1,-1,4)
If an edge is already present in the network, then only its
weights or other attribute are updated. If the graph is already
weighted, then the attr="weight"
setting is implicit, and
one does not need to give it explicitly.
Deleting edges. The replacement syntax allow the deletion of
edges, by specifying FALSE
or NULL
as the
replacement value:
graph[v, w] <- FALSE
removes the edge from vertex v to vertex w. As this can be used to delete edges between two sets of vertices, either pairwise:
graph[from=v, to=w] <- FALSE
or not:
graph[v, w] <- FALSE
if v and w are vectors of edge ids or names.
‘[
’ allows logical indices and negative indices as well,
with the usual R semantics. E.g.
graph[degree(graph)==0, 1] <- 1
adds an edge from every isolate vertex to vertex one, and
G <- make_empty_graph(10) G[-1,1] <- TRUE
creates a star graph.
Of course, the indexing operators support vertex names,
so instead of a numeric vertex id a vertex can also be given to
‘[
’ and ‘[[
’.
A scalar or matrix. See details below.
Other structural queries:
[[.igraph()
,
adjacent_vertices()
,
are_adjacent()
,
ends()
,
get.edge.ids()
,
gorder()
,
gsize()
,
head_of()
,
incident_edges()
,
incident()
,
is_directed()
,
neighbors()
,
tail_of()