A fast community detection method in bipartite networks by distance dynamics

Sun, Hong-Liang; Ch'ng, Eugene; Yong, Xi; Garibaldi, Jonathan M.; See, Simon; Chen, Duan-Bing

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(jEj) in sparse networks, where jEj 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.

Citation

Sun, H., Ch'ng, E., Yong, X., Garibaldi, J. M., See, S., & Chen, D. (2018). A fast community detection method in bipartite networks by distance dynamics. Physica A: Statistical Mechanics and its Applications, 496, https://doi.org/10.1016/j.physa.2017.12.099

• Journal Article Type: Article
• Acceptance Date: Dec 19, 2017
• Online Publication Date: Dec 30, 2017
• Publication Date: Apr 15, 2018
• Deposit Date: Dec 21, 2017
• Publicly Available Date: Dec 31, 2018
• Journal: Physica A: Statistical Mechanics and its Applications
• Print ISSN: 0378-4371
• Electronic ISSN: 0378-4371
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 496
• DOI: https://doi.org/10.1016/j.physa.2017.12.099
• Keywords: Node similarity; Community detection; Large bipartite networks
• Public URL: https://nottingham-repository.worktribe.com/output/925086
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0378437117313481