bipartiteSBM

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This code and data repository accompanies the paper

• Community Detection in Bipartite Networks with Stochastic Blockmodels, Tzu-Chi Yen_ and Daniel B. Larremore_, Physical Review E 102, 032309, (2020).

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.

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

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

• The project documentation is at https://docs.netscied.tw/bipartiteSBM/index.html.
• For installation instructions, see https://docs.netscied.tw/bipartiteSBM/usage/installation.html. You'll need CMake_ and Boost_ libraries, and a compiler that supports C++14.

.. _MCMC sampler: https://github.com/junipertcy/bipartiteSBM-MCMC .. _Kernighan-Lin algorithm: https://github.com/junipertcy/bipartiteSBM-KL .. _CMake: https://cmake.org/ .. _Boost: https://www.boost.org/ .. _Tzu-Chi Yen: https://junipertcy.info/ .. _Daniel B. Larremore: https://larremorelab.github.io/ .. _arXiv: https://arxiv.org/abs/2001.11818 .. _PRE: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.032309 .. _issues: https://github.com/junipertcy/bipartiteSBM/issues .. _docs: https://docs.netscied.tw/bipartiteSBM/index.html