Searching for Communities in Bipartite Networks

TL;DR

This paper introduces a specialized modularity measure and an algorithm for detecting communities in bipartite networks, validated on research collaboration data and compared using information-theoretic methods.

Contribution

It presents a novel bipartite-specific modularity measure and an associated algorithm for community detection in bipartite networks.

Findings

Identified meaningful communities in EU research networks

The bipartite modularity outperforms traditional methods

Community structures are validated with information-theoretic comparisons

Abstract

Bipartite networks are a useful tool for representing and investigating interaction networks. We consider methods for identifying communities in bipartite networks. Intuitive notions of network community groups are made explicit using Newman's modularity measure. A specialized version of the modularity, adapted to be appropriate for bipartite networks, is presented; a corresponding algorithm is described for identifying community groups through maximizing this measure. The algorithm is applied to networks derived from the EU Framework Programs on Research and Technological Development. Community groups identified are compared using information-theoretic methods.