Efficient Bayesian Community Detection using Non-negative Matrix Factorisation
Machine Learning
2010-09-28 v5 Statistical Mechanics
Physics and Society
Abstract
Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matrix factorisation (NMF) model to produce a probabilistic output for node memberships. The scheme has the advantage of computational efficiency, soft community membership and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection. Our approach performs favourably compared to other methods at a fraction of the computational costs.
Cite
@article{arxiv.1009.2646,
title = {Efficient Bayesian Community Detection using Non-negative Matrix Factorisation},
author = {Ioannis Psorakis and Stephen Roberts and Ben Sheldon},
journal= {arXiv preprint arXiv:1009.2646},
year = {2010}
}