Overlapping Community Detection in Bipartite Networks

TL;DR

This paper introduces BiTector, an efficient algorithm for detecting overlapping communities in large-scale bipartite networks, relying solely on network topology without prior knowledge.

Contribution

The paper presents a novel, topology-based algorithm for overlapping community detection in bipartite networks, applicable to large-scale real-world data.

Findings

Successfully identifies overlapping communities in real-world bipartite networks

Operates efficiently on large-scale sparse networks

Does not require prior knowledge of community structure

Abstract

Recent researches have discovered that rich interactions among entities in nature and society bring about complex networks with community structures. Although the investigation of the community structures has promoted the development of many successful algorithms, most of them only find separated communities, while for the vast majority of real-world networks, communities actually overlap to some extent. Moreover, the vertices of networks can often belong to different domains as well. Therefore, in this paper, we propose a novel algorithm BiTector Bi-community De-tector) to efficiently mine overlapping communities in large-scale sparse bipartite networks. It only depends on the network topology, and does not require any priori knowledge about the number or the original partition of the network. We apply the algorithm to real-world data from different domains, showing that BiTector can successfully identifies the overlapping community structures of the bipartite networks.