cdlib.algorithms.belief

belief(g_original, max_it=100, eps=0.0001, reruns_if_not_conv=5, threshold=0.005, q_max=7)

Belief community seeks the consensus of many high-modularity partitions. It does this with a scalable message-passing algorithm, derived by treating the modularity as a Hamiltonian and applying the cavity method.

Parameters:

• g_original – a networkx/igraph object
• max_it –
• eps –
• reruns_if_not_conv –
• threshold –
• q_max –

Returns:

NodeClustering object

Example:

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

References:

Zhang, Pan, and Cristopher Moore. “Scalable detection of statistically significant communities and hierarchies, using message passing for modularity.” Proceedings of the National Academy of Sciences 111.51 (2014): 18144-18149.

Note

Reference implementation: https://github.com/weberfm/belief_propagation_community_detection