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Heuristic and inference engine for estimating the number of communities in a bipartite network
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README.rst

bipartiteSBM

Twitter: @oneofyen License Build Status

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.

https://wiki.junipertcy.info/images/1/10/Det_k_bisbm-logo.png

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.

Documentation

The project documentation is at https://docs.netscied.tw/bipartiteSBM/index.html.

Companion article

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.

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