title: Overlapping community detection using Bayesian non-negative matrix factorization.
abstract: Identifying overlapping communities in networks is a challenging task. In this work we present a probabilistic approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract overlapping modules from a network. The scheme has the advantage of soft-partitioning solutions, assignment of node participation scores to modules, 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.
authors.affiliation: Pattern Analysis and Machine Learning Research Group, Department of Engineering Science, University of Oxford, Oxford, United Kingdom. ioannis.psorakis@eng.ox.ac.uk
authors.forename: Ioannis
authors.initials: I
authors.lastname: Psorakis
authors.forename: Stephen
authors.initials: S
authors.lastname: Roberts
authors.forename: Mark
authors.initials: M
authors.lastname: Ebden
authors.forename: Ben
authors.initials: B
authors.lastname: Sheldon