cdlib.algorithms.sbm_dl¶

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 = sbm_dl(G)

References:

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.

Note

Implementation from graph-tool library, please report to https://graph-tool.skewed.de for details