Parameter-free Community Detection through Distance Dynamic Synchronization. (23rd February 2019)

• Record Type: Journal Article
• Authors: Huang, Jianbin; Bian, Qingquan; Sun, Heli; Yang, Yaming; Zhou, Yu
• Editors: Murtagh, Fionn
• Is Part Of: Computer journal. Volume 62:Number 11(2019)
• Page Start: 1625
• Page End: 1638
• Publication Date: 2019-02-23
• Subjects: community detection -- parameter-free -- distance synchronization; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxy080
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Community detection plays a significant role in understanding the essence of a network. A recently proposed algorithm Attractor, which is based on distance dynamics, can spot communities effectively, but it depends on a cohesion parameter. Moreover, no efficient way is provided to find an optimal cohesion parameter setting. In this paper, we propose a parameter-free community detection algorithm by synchronizing distances iteratively. In each iteration, the distance of each edge will change dynamically according to the effect generated by its related neighbours. Several iterations later, distances between vertices belonging to the same community will synchronize to 0, while distances between vertices not in the same community will synchronize to 1. Besides, merging and division strategies are built up in the process of community detection. Experiments on both real-world and synthetic networks demonstrate benefits of our method compared to the baseline methods.