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

Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. (nested)

Fit a nested non-overlapping stochastic block model (SBM) by minimizing its description length using an agglomerative heuristic. Return the lowest level found. Currently cdlib do not support hierarchical clustering.

Supported Graph Types

Undirected Directed Weighted
Yes No No
Parameters:g_original – igraph/networkx object
Returns:NodeClustering object
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.sbm_dl(G)

Tiago P. Peixoto, “Hierarchical block structures and high-resolution model selection in large networks”, Physical Review X 4.1 (2014): 011047


Implementation from graph-tool library, please report to for details