junipertcy / bipartiteSBM

A Bayesian model+algorithm for community detection in bipartite networks

Home Page:https://docs.netscied.tw/bipartiteSBM/index.html

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bipartiteSBM

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This code and data repository accompanies the paper

Read it on: [arXiv] or [PRE].

The code is tested on Python>=3.6. For questions, please email tzuchi at tzuchi.yen@colorado.edu, or via the issues!

Introduction

The bipartiteSBM implements a fast community inference algorithm for the bipartite Stochastic Block Model (biSBM) using the MCMC sampler or the Kernighan-Lin algorithm as the core optimization engine. It searches through the space with dynamic programming, and 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. (det_k_bisbm is a deprecated name for the same library.)

The bipartiteSBM utilizes the Minimum Description Length principle to determine a point estimate of the bipartite partition that best compresses the model and data. In other words, we formulate priors and maximize the corresponding posterior likelihood function.

Several test examples are included. Read on in the docs!

Documentation

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A Bayesian model+algorithm for community detection in bipartite networks

https://docs.netscied.tw/bipartiteSBM/index.html

License:GNU General Public License v3.0


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