Wmixnet: Software for Clustering the Nodes of Binary and Valued Graphs using the Stochastic Block Model
Computation
2014-02-17 v1 Applications
Abstract
Clustering the nodes of a graph allows the analysis of the topology of a network. The stochastic block model is a clustering method based on a probabilistic model. Initially developed for binary networks it has recently been extended to valued networks possibly with covariates on the edges. We present an implementation of a variational EM algorithm. It is written using C++, parallelized, available under a GNU General Public License (version 3), and can select the optimal number of clusters using the ICL criteria. It allows us to analyze networks with ten thousand nodes in a reasonable amount of time.
Cite
@article{arxiv.1402.3410,
title = {Wmixnet: Software for Clustering the Nodes of Binary and Valued Graphs using the Stochastic Block Model},
author = {Jean-Benoist Leger},
journal= {arXiv preprint arXiv:1402.3410},
year = {2014}
}