Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilizes a Bayesian nonnegative matrix factorization (NMF) 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.
Another example is the method proposed by Psorakis et al. REF , which applies a Bayesian non-negative matrix factorization model to extract overlapping modules from networks.
Overlapping community detection using Bayesian non-negative matrix factorization
Physical review. E, Statistical, nonlinear, and soft matter physics
Physical review. E, Statistical, nonlinear, and soft matter physics
Physics Medicine