Balanced multi-label propagation for overlapping community detection in social networks

Wu Zhi-Hao, Lin You-Fang, S Gregory, Wan Huai-Yu, Tian Sheng-Feng

Research output: Contribution to journalArticle (Academic Journal)

95 Citations (Scopus)

Abstract

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.
Translated title of the contributionBalanced multi-label propagation for overlapping community detection in social networks
Original languageEnglish
Pages (from-to)468 - 479
Number of pages12
JournalJournal of Computer Science and Technology
Volume27
DOIs
Publication statusPublished - May 2012

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