Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (3): 861-871.doi: 10.23940/ijpe.19.03.p15.861871

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Using Community Detection to Discover Opinion Leaders in Social Circles

Huajiang Mena, Xiaoyu Jib, *, and Wei Wangb   

  1. a School of Electronic and Information Engineering, Beihang University, Beijing, 100083, China;
    b Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China
  • Submitted on ; Revised on ;
  • Contact: 16120380@bjtu.edu.cn
  • About author:Huajiang Men is currently a Ph.D. student of engineering in the School of Electronic and Information Engineering at Beihang University. He received his M.S. degree from Dalian Maritime University in 2002. He is mainly engaged in research on mobile communication security such as 3G/4G/5G.Xiaoyu Ji is currently a M.S. student in the School of Computer and Information Technology at Beijing Jiaotong University. He received his B.S. degree from Shandong Agricultural University in 2016. His main research interests include social networks.Wei Wang is a full professor in the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation in the School of Computer and Information Technology at Beijing Jiaotong University. He earned his Ph.D. from Xi'an Jiaotong University in 2005. He was a postdoctoral researcher at the University of Trento, Italy, from 2005-2006 and at TELECOM Bretagne and at INRIA, France, from 2007-2008. His research interests include network security and data mining.

Abstract: Discovering opinion leaders in social circles is an important issue in social networks. Most existing opinion leader detection methods usually focus on the whole social network. However, the composition of social networks is complicated, as many social circles or communities based on interests exist in social networks. We find that it is hard to find all the opinion leaders of small social circles if we only focus on the whole network. In this work, we propose a method in which we conduct community detection first and then perform influence analysis on the communities to find the opinion leaders of social circles. Most previous overlapping community detection methods are usually time-consuming and cannot output results in acceptable time on a large-scale dataset; therefore, we propose a linear time complexity overlapping community detection method based on topic graph. We calculate degree centrality, betweenness centrality, closeness centrality, and PageRank value of the nodes in each community detected to find opinion leaders. We collect a large-scale dataset from Zhihu and use it to validate our methods. The extensive results demonstrate that our method can produce better results in finding opinion leaders in social circles compared with other methods.

Key words: opinion leader, topic, influence, social network, community