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.
The project documentation is at https://docs.netscied.tw/bipartiteSBM/index.html.
If you find
bipartiteSBM useful for your research, please consider citing the following paper:
Tzu-Chi Yen and Daniel B. Larremore, "Blockmodeling a Bipartite Network with Bipartite Prior," in preparation.