Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks.

Modeling and estimating mixed memberships for overlapping unipartite un-weighted networks has been well studied in recent years. However, to our knowledge, there is no model for a more general case, the overlapping bipartite weighted networks. To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model. Our model allows an adjacency matrix to follow any distribution as long as its expectation has a block structure related to node membership. In particular, BiMMDF can model overlapping bipartite signed networks and it is an extension of many previous models, including the popular mixed membership stochastic blockmodels. An efficient algorithm with a theoretical guarantee of consistent estimation is applied to fit BiMMDF. We then obtain the separation conditions of BiMMDF for different distributions. Furthermore, we also consider missing edges for sparse networks. The advantage of BiMMDF is demonstrated in extensive synthetic networks and eight real-world networks. • We propose a novel model BiMMDF for overlapping bipartite weighted networks. • We use an algorithm with a theoretical guarantee of consistency to fit BiMMDF. • Separation conditions of BiMMDF for different distributions are analyzed. • We also consider missing edges for sparse networks. • We conduct substantial experiments to demonstrate the advantage of BiMMDF. [ABSTRACT FROM AUTHOR]