2010/09/14 by Psorakis, Ioannis, Roberts, Stephen, Sheldon, Ben
#FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Physics and Society (physics.soc-ph) #Statistical Mechanics (cond-mat.stat-mech)
paper · doi:10.48550/arxiv.1009.2646
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.