sbm_dl(g_original: object) → cdlib.classes.node_clustering.NodeClustering¶
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models.
Fit a non-overlapping stochastic block model (SBM) by minimizing its description length using an agglomerative heuristic.
Supported Graph Types
Undirected Directed Weighted Yes No No Parameters: g_original – network/igraph object Returns: NodeClustering object Example:
>>> from cdlib import algorithms >>> import networkx as nx >>> G = nx.karate_club_graph() >>> coms = algorithms.sbm_dl(G)
Tiago P. Peixoto, “Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models”, Phys. Rev. E 89, 012804 (2014), DOI: 10.1103/PhysRevE.89.012804 [sci-hub, @tor], arXiv: 1310.4378.
Implementation from graph-tool library, please report to https://graph-tool.skewed.de for details