Modularity and community detection in bipartite networks

TL;DR

This paper introduces a new bipartite modularity measure, along with an algorithm for detecting community structures in bipartite networks, validated on real-world data.

Contribution

It defines a bipartite-specific modularity and develops an eigenspectrum-based algorithm for community detection in bipartite networks.

Findings

Successfully identifies modular structures in real-world bipartite networks

The bipartite modularity provides a meaningful measure of community structure

The algorithm outperforms existing methods in bipartite community detection

Abstract

The modularity of a network quantifies the extent, relative to a null model network, to which vertices cluster into community groups. We define a null model appropriate for bipartite networks, and use it to define a bipartite modularity. The bipartite modularity is presented in terms of a modularity matrix B; some key properties of the eigenspectrum of B are identified and used to describe an algorithm for identifying modules in bipartite networks. The algorithm is based on the idea that the modules in the two parts of the network are dependent, with each part mutually being used to induce the vertices for the other part into the modules. We apply the algorithm to real-world network data, showing that the algorithm successfully identifies the modular structure of bipartite networks.