The bipartiteSBM User Guide

The bipartiteSBM is a Python library of a fast community inference heuristic of the bipartite Stochastic Block Model (biSBM), using the MCMC sampler or the Kernighan-Lin algorithm. It estimates the number of communities (as well as the partition) for a bipartite network.

The bipartiteSBM helps you infer the number of communities in a bipartite network.

The bipartiteSBM utilizes the Minimum Description Length principle to determine a point estimate of the numbers of communities in the biSBM that best compress the model and data. Several test examples are included.

Supported and tested on Python>=3.6.

If you have any questions, please contact

Module documentation

Frequently Asked Questions (FAQ)

Additional resources


The bipartiteSBM is inspired and supported by the following great humans, Daniel B. Larremore, Tiago de Paula Peixoto, Jean-Gabriel Young, Pan Zhang, and Jie Tang. Thanks Valentin Haenel who helped debug and fix the Numba code.