Using community detection to discover opinion leaders in social circles

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. © 2019 Totem Publisher, Inc. All rights reserved.