›› 2012, Vol. 27 ›› Issue (3): 468-479.doi: 10.1007/s11390-012-1236-x

• Special Issue on Social Network Mining • Previous Articles     Next Articles

Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks

Zhi-Hao Wu1 (武志昊), You-Fang Lin1 (林友芳), Steve Gregory2, Huai-Yu Wan1 (万怀宇), Student Member, CCF, and Sheng-Feng Tian1 (田盛丰)   

  1. 1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
    2. Department of Computer Science, University of Bristol, Bristol BS8 1UB, U.K.
  • Received:2011-08-31 Revised:2012-01-06 Online:2012-05-05 Published:2012-05-05
  • About author:Zhi-Hao Wu received the B.Sc. degree in computer science and tech-nology from Beijing Jiaotong Uni-versity (BJTU) in 2007. Now he is a Ph.D. candidate in computer sci-ence and technology at BJTU. His re-search interests include complex net-works and social networks analysis, data mining and machine learning. His current research focuses on com-munity detection and evolution in networks.
  • Supported by:

    This work was partially supported by the Fundamental Research Funds for the Central Universities of China, the National Natural Science Foundation of China under Grant No. 60905029, the Natural Science Foundation of Beijing of China under Grant No. 4112046.

In this paper, we propose a balanced multi-label propagation algorithm (BMLPA) for overlapping community detection in social networks. As well as its fast speed, another important advantage of our method is good stability, which other multi-label propagation algorithms, such as COPRA, lack. In BMLPA, we propose a new update strategy, which requires that community identifiers of one vertex should have balanced belonging coefficients. The advantage of this strategy is that it allows vertices to belong to any number of communities without a global limit on the largest number of community memberships, which is needed for COPRA. Also, we propose a fast method to generate "rough cores", which can be used to initialize labels for multi-label propagation algorithms, and are able to improve the quality and stability of results. Experimental results on synthetic and real social networks show that BMLPA is very efficient and effective for uncovering overlapping communities.

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