Community Detection in Bipartite Networks with Stochastic Blockmodels

Abstract

In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly flexible generative model for networks with block structure, an intuitive choice for bipartite community detection. However, typical formulations of the SBM do not make use of the special structure of bipartite networks. In this work, we introduce a Bayesian nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks without overfitting. The biSBM improves community detection results over general SBMs when data are noisy, improves the model resolution limit by a factor of √ 2, and expands our understanding of the complicated optimization landscape associated with community detection tasks. A direct comparison of certain terms of the prior distributions in the biSBM and a related high-resolution hierarchical SBM also reveals a counterintuitive regime of community detection problems, populated by smaller and sparser networks, where non-hierarchical models outperform their more flexible counterpart.

Mapping Flows on Bipartite Networks

“…First, unipartite projections of bipartite networks cannot preserve all the information that is encoded in the bipartite network such that significant structure is lost [2]. Second, applying unipartite methods directly to bipartite networks ignores the regularities of bipartite networks and does not take into account the fact that links only connect nodes of different types [7]. What do we miss by discarding this node-type information?…”