  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.
