A Triad Percolation Method for Detecting Communities in Social Networks. (26th November 2018)

• Record Type: Journal Article
• Authors: Zhang, Zhiwei; Cui, Lin; Pan, Zhenggao; Fang, Aidong; Zhang, Haiyang
• Is Part Of: Data science journal. Volume 17(2018)
• Publication Date: 2018-11-26
• Subjects: Community detection -- Network analysis -- Complex network -- Social sciences computing -- Triad percolation -- Graph theory; Science -- Data processing -- Periodicals; Database management -- Periodicals; 502.85
• DOI: 10.5334/dsj-2018-030
• Languages: English
• ISSNs: 1683-1470

Abstract

For the purpose of detecting communities in social networks, a triad percolation method is proposed, which first locates all close-triads and open-triads from a social network, then a specified close-triad or open-triad is selected as the seed to expand by utilizing the triad percolation method, such that a community is found when this expanding process meet a particular threshold. This approach can efficiently detect communities not only from a densely social network, but also from the sparsely one. Experimental results performing on real-world social benchmark networks and artificially simulated networks give a satisfactory correspondence.