The clustering of the vertex set of a graph.
This class extends Clustering
by linking it to a specific Graph
object and by optionally storing the modularity score of the clustering. It also provides some handy methods like getting the subgraph corresponding to a cluster and such.
Note  
since this class is linked to a Graph , destroying the graph by the del operator does not free the memory occupied by the graph if there exists a VertexClustering that references the Graph . 
Class Method 

Creates a vertex clustering based on the value of a vertex attribute. 
Method  __init__ 
Creates a clustering object for a given graph. 
Method  __plot__ 
Plots the clustering to the given Cairo context in the given bounding box. 
Method  as 
Returns a VertexCover that contains the same clusters as this clustering. 
Method  cluster 
Returns a graph where each cluster is contracted into a single vertex. 
Method  crossing 
Returns a boolean vector where element i is True iff edge i lies between clusters, False otherwise. 
Method  giant 
Returns the largest cluster of the clustered graph. 
Method  recalculate 
Recalculates the stored modularity value. 
Method  subgraph 
Get the subgraph belonging to a given cluster. 
Method  subgraphs 
Gets all the subgraphs belonging to each of the clusters. 
Property  graph 
Returns the graph belonging to this object 
Property  modularity 
Returns the modularity score 
Method  _formatted 
Iterates over the clusters and formats them into a string to be presented in the summary. 
Method  _recalculate 
Recalculates the stored modularity value and swallows all exceptions raised by the modularity function (if any). 
Class Variable  _default 
Undocumented 
Instance Variable  _graph 
Undocumented 
Instance Variable  _modularity 
Undocumented 
Instance Variable  _modularity 
Undocumented 
Instance Variable  _modularity 
Undocumented 
Inherited from Clustering
:
Method  __getitem__ 
Returns the members of the specified cluster. 
Method  __iter__ 
Iterates over the clusters in this clustering. 
Method  __len__ 
Returns the number of clusters. 
Method  __str__ 
Undocumented 
Method  compare 
Compares this clustering to another one using some similarity or distance metric. 
Method  size 
Returns the size of a given cluster. 
Method  size 
Returns the histogram of cluster sizes. 
Method  sizes 
Returns the size of given clusters. 
Method  summary 
Returns the summary of the clustering. 
Property  membership 
Returns the membership vector. 
Property  n 
Returns the number of elements covered by this clustering. 
Instance Variable  _len 
Undocumented 
Instance Variable  _membership 
Undocumented 
Creates a vertex clustering based on the value of a vertex attribute.
Vertices having the same attribute will correspond to the same cluster.
Parameters  
graph  the graph on which we are working 
attribute  name of the attribute on which the clustering is based. 
intervals  for numeric attributes, you can either pass a single number or a list of numbers here. A single number means that the vertices will be put in bins of that width and vertices ending up in the same bin will be in the same cluster. A list of numbers specify the bin positions explicitly; e.g., [10, 20, 30] means that there will be four categories: vertices with the attribute value less than 10, between 10 and 20, between 20 and 30 and over 30. Intervals are closed from the left and open from the right. 
params  additional parameters to be stored in this object. 
Returns  
a new VertexClustering object 
igraph.clustering.Clustering.__init__
igraph.cut.Cut
Creates a clustering object for a given graph.
Parameters  
graph  the graph that will be associated to the clustering 
membership  the membership list. The length of the list must be equal to the number of vertices in the graph. If None, every vertex is assumed to belong to the same cluster. 
modularity  the modularity score of the clustering. If None, it will be calculated when needed. 
params  additional parameters to be stored in this object. 
modularity  arguments that should be passed to Graph.modularity when the modularity is (re)calculated. If the original graph was weighted, you should pass a dictionary containing a weight key with the appropriate value here. 
Plots the clustering to the given Cairo context in the given bounding box.
This is done by calling Graph.__plot__()
with the same arguments, but coloring the graph vertices according to the current clustering (unless overridden by the vertex_color argument explicitly).
This method understands all the positional and keyword arguments that are understood by Graph.__plot__()
, only the differences will be highlighted here:
mark_groups: whether to highlight some of the vertex groups by colored polygons. Besides the values accepted by
Graph.__plot__
(i.e., a dict mapping colors to vertex indices, a list containing lists of vertex indices, or False), the following are also accepted: True: all the groups will be highlighted, the colors matching the corresponding color indices from the current palette (see the palette keyword argument of
Graph.__plot__
.  A dict mapping cluster indices or tuples of vertex indices to color names. The given clusters or vertex groups will be highlighted by the given colors.
 A list of cluster indices. This is equivalent to passing a dict mapping numeric color indices from the current palette to cluster indices; therefore, the cluster referred to by element i of the list will be highlighted by color i from the palette.
The value of the plotting.mark_groups configuration key is also taken into account here; if that configuration key is True and mark_groups is not given explicitly, it will automatically be set to True.
In place of lists of vertex indices, you may also use
VertexSeq
instances.In place of color names, you may also use color indices into the current palette. None as a color name will mean that the corresponding group is ignored.
 True: all the groups will be highlighted, the colors matching the corresponding color indices from the current palette (see the palette keyword argument of
palette: the palette used to resolve numeric color indices to RGBA values. By default, this is an instance of
ClusterColoringPalette
.
See Also  
Graph.__plot__() for more supported keyword arguments. 
igraph.clustering.Clustering.as_cover
Returns a VertexCover
that contains the same clusters as this clustering.
Returns a graph where each cluster is contracted into a single vertex.
In the resulting graph, vertex i represents cluster i in this clustering. Vertex i and j will be connected if there was at least one connected vertex pair (a, b) in the original graph such that vertex a was in cluster i and vertex b was in cluster j.
Parameters  
combine  specifies how to derive the attributes of the vertices in the new graph from the attributes of the old ones. See Graph.contract_vertices() for more details. 
combine  specifies how to derive the attributes of the edges in the new graph from the attributes of the old ones. See Graph.simplify() for more details. If you specify False here, edges will not be combined, and the number of edges between the vertices representing the original clusters will be equal to the number of edges between the members of those clusters in the original graph. 
Returns  
the new graph. 
Returns the largest cluster of the clustered graph.
The largest cluster is a cluster for which no larger cluster exists in the clustering. It may also be known as the giant community if the clustering represents the result of a community detection function.
Precondition: the vertex set of the graph hasn't been modified since the moment the cover was constructed.
Returns  
a copy of the largest cluster.  
Note  
there can be multiple largest clusters, this method will return the copy of an arbitrary one if there are multiple largest clusters. 
Recalculates the stored modularity value.
This method must be called before querying the modularity score of the clustering through the class member modularity or q if the graph has been modified (edges have been added or removed) since the creation of the VertexClustering
object.
Returns  
the new modularity score 
Get the subgraph belonging to a given cluster.
Precondition: the vertex set of the graph hasn't been modified since the moment the cover was constructed.
Parameters  
idx  the cluster index 
Returns  
a copy of the subgraph 
Gets all the subgraphs belonging to each of the clusters.
Precondition: the vertex set of the graph hasn't been modified since the moment the cover was constructed.
Returns  
a list containing copies of the subgraphs 