Title: Matched bipartite block model with covariates,
Abstract: Community detection or clustering is a fundamental task in the analysis of
network data. Many real networks have a bipartite structure which makes
community detection challenging. In this paper, we consider a model which
allows for matched communities in the bipartite setting, in addition to node
covariates with information about the matching. We derive a simple fast
algorithm for fitting the model based on variational inference ideas and show
its effectiveness on both simulated and real data. A variation of the model to
allow for degree-correction is also considered, in addition to a novel approach
to fitting such degree-corrected models.