Detecting overlapping communities in massive networks

• Published in: Europhysics letters Vol. 108; no. 6; pp. 68001 - p1-68001-p6
• Main Authors: Sun, Bing-Jie, Shen, Hua-Wei, Cheng, Xue-Qi
• Format: Journal Article
• Language: English
• Published: Les Ulis EDP Sciences, IOP Publishing and Società Italiana di Fisica 01.12.2014; IOP Publishing
• Subjects: 05.65.+b; 89.75.Fb; 89.75.Kd; Balances (scales); Communities; Handles; Network analysis; Networks; Nodes; Optimization; State of the art
• ISSN: 0295-5075; 1286-4854
• DOI: 10.1209/0295-5075/108/68001

Community detection is an essential work for network analysis. However, few methods could be used as off-the-shelf tools to detect communities in real-world networks for two main reasons: Real networks often contain millions of nodes or even hundreds of millions of nodes while most methods cannot handle networks at this scale. One node often belongs to multiple communities, posing another big challenge. In this paper, we circumvent the tricky problem of detecting overlapping communities using a two-stage framework, balancing efficiency and accuracy. Given a network, we first focus on efficiently finding its coarse-grained communities. Starting from them, we next obtain overlapping communities by optimizing a principled objective function. In this divide-and-conquer way, the framework achieves a much better performance than detecting overlapping communities from scratch. Extensive tests on synthetic and real networks demonstrate that it outperforms state-of-the-art methods in terms of both efficiency and accuracy.