Communities¶
Functions for computing and measuring community structure.
The functions in this class are not imported into the top-level
networkx
namespace. You can access these functions by importing
the networkx.algorithms.community
module, then accessing the
functions as attributes of community
. For example:
>>> from networkx.algorithms import community
>>> G = nx.barbell_graph(5, 1)
>>> communities_generator = 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.
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Partition a graph into two blocks using the Kernighan–Lin algorithm. |
K-Clique¶
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Find k-clique communities in graph using the percolation method. |
Modularity-based communities¶
Functions for detecting communities based on modularity.
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Find communities in graph using Clauset-Newman-Moore greedy modularity maximization. |
Find communities in graph using the greedy modularity maximization. |
Tree partitioning¶
Lukes Algorithm for exact optimal weighted tree partitioning.
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Optimal partitioning of a weighted tree using the Lukes algorithm. |
Label propagation¶
Label propagation community detection algorithms.
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Returns communities in |
Generates community sets determined by label propagation |
Fluid Communities¶
Asynchronous Fluid Communities algorithm for community detection.
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Returns communities in |
Measuring partitions¶
Functions for measuring the quality of a partition (into communities).
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Returns the coverage of a partition. |
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Returns the modularity of the given partition of the graph. |
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Returns the performance of a partition. |
Partitions via centrality measures¶
Functions for computing communities based on centrality notions.
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Finds communities in a graph using the Girvan–Newman method. |
Validating partitions¶
Helper functions for community-finding algorithms.
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Returns True if |