belief(g_original: object, max_it: int = 100, eps: float = 0.0001, reruns_if_not_conv: int = 5, threshold: float = 0.005, q_max: int = 7) → cdlib.classes.node_clustering.NodeClustering¶
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.
Supported Graph Types
Undirected Directed Weighted Yes No No Parameters:
- g_original – a networkx/igraph object
- max_it –
- eps –
- reruns_if_not_conv –
- threshold –
- q_max –
>>> from cdlib import algorithms >>> import networkx as nx >>> G = nx.karate_club_graph() >>> coms = algorithms.belief(G)
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.
Reference implementation: https://github.com/weberfm/belief_propagation_community_detection