A fast community detection method in bipartite networks by distance dynamics

Hong liang Sun, Eugene Ch'ng, Xi Yong, Jonathan M. Garibaldi, Simon See, Duan bing Chen

Research output: Journal PublicationArticlepeer-review

25 Citations (Scopus)

Abstract

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(|E|) in sparse networks, where |E| is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time.

Original languageEnglish
Pages (from-to)108-120
Number of pages13
JournalPhysica A: Statistical Mechanics and its Applications
Volume496
DOIs
Publication statusPublished - 15 Apr 2018

Keywords

  • Community detection
  • Large bipartite networks
  • Node similarity

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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