cdlib.algorithms.percomvc

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

The PercoMVC approach composes of two steps. In the first step, the algorithm attempts to determine all communities that the clique percolation algorithm may find. In the second step, the algorithm computes the Eigenvector Centrality method on the output of the first step to measure the influence of network nodes and reduce the rate of the unclassified nodes

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.percomvc(G)

References:

Kasoro, Nathanaël, et al. “PercoMCV: A hybrid approach of community detection in social networks.” Procedia Computer Science 151 (2019): 45-52.

Note

Reference implementation: https://github.com/sedjokas/PercoMCV-Code-source