cdlib.algorithms.ebgc

ebgc(g_original: object) → cdlib.classes.node_clustering.NodeClustering

The entropy-based clustering approach finds locally optimal clusters by growing a random seed in a manner that minimizes graph entropy.

Supported Graph Types

Undirected	Directed	Weighted
Yes	No	No

Parameters:g_original – a networkx/igraph object Returns:NodeClustering object Example:

>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.ebgc(G)

References:

Kenley, Edward Casey, and Young-Rae Cho. “Entropy-based graph clustering: Application to biological and social networks.” 2011 IEEE 11th International Conference on Data Mining. IEEE, 2011.

Note

Reference Implementation: https://github.com/SubaiDeng/EBGC-Entropy-Based-Graph-Clustering