Matched bipartite block model with covariates
This work addresses the challenge of community detection in bipartite networks, which is incremental as it builds on existing models by adding covariates and degree-correction.
The paper tackles community detection in bipartite networks by introducing a model that incorporates matched communities and node covariates, and presents a fast variational inference algorithm that shows effectiveness on simulated and real data.
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.