A Bayesian model+algorithm for community detection in bipartite networks
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This code and data repository accompanies the paper
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 [email protected], or via the issues
_!
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
_!
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