# 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 tzuchi.yen@colorado.edu.

Module documentation

Frequently Asked Questions (FAQ)

Additional resources

## Acknowledgements¶

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.