Communities#
Functions for computing and measuring community structure.
The community
subpackage can be accessed by using networkx.community
, then accessing the
functions as attributes of community
. For example:
>>> import networkx as nx
>>> G = nx.barbell_graph(5, 1)
>>> communities_generator = nx.community.girvan_newman(G)
>>> top_level_communities = next(communities_generator)
>>> next_level_communities = next(communities_generator)
>>> sorted(map(sorted, next_level_communities))
[[0, 1, 2, 3, 4], [5], [6, 7, 8, 9, 10]]
Bipartitions#
Functions for computing the Kernighan–Lin bipartition algorithm.

Partition a graph into two blocks using the Kernighan–Lin algorithm. 
Divisive Communities#

Partition created by iteratively removing the highest edge betweenness edge. 
Partition created by removing the highest edge current flow betweenness edge. 
KClique#

Find kclique communities in graph using the percolation method. 
Modularitybased communities#
Functions for detecting communities based on modularity.

Find communities in G using greedy modularity maximization. 

Find communities in G using greedy modularity maximization. 
Tree partitioning#
Lukes Algorithm for exact optimal weighted tree partitioning.

Optimal partitioning of a weighted tree using the Lukes algorithm. 
Label propagation#
Label propagation community detection algorithms.

Returns communities in 
Generates community sets determined by label propagation 


Returns communities in 
Louvain Community Detection#
Function for detecting communities based on Louvain Community Detection Algorithm

Find the best partition of a graph using the Louvain Community Detection Algorithm. 

Yields partitions for each level of the Louvain Community Detection Algorithm 
Fluid Communities#
Asynchronous Fluid Communities algorithm for community detection.

Returns communities in 
Measuring partitions#
Functions for measuring the quality of a partition (into communities).

Returns the modularity of the given partition of the graph. 

Returns the coverage and performance of a partition of G. 
Partitions via centrality measures#
Functions for computing communities based on centrality notions.

Finds communities in a graph using the Girvan–Newman method. 
Validating partitions#
Helper functions for communityfinding algorithms.

Returns True if 