bipartiteSBM User Guide¶
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.
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 email@example.com.
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.