cdlib.algorithms.sbm_dl_nested

cdlib.algorithms.sbm_dl_nested(g_original: object) → 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

Example:

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

References:

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

Note

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