Opinion leaders discovery in dynamic social network

Abstract

Recently, several studies are focused on mining opinion leader in social network because of its widespread applications. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. However, in real-life applications, social network are usually evolved with time; few prior research efforts have been elaborated on finding opinion leaders with dynamic concept. In this study, a novel algorithm, D_OLMiner, is proposed to efficiently find the opinion leaders from a dynamic social network. We utilize a network emerging method to construct a dynamic social network, and then detect the community structure to solve the influence overlapping issue and reduce the computation time. The experimental results show that the proposed D_OLMiner can effectively discover the influential opinion leaders in real dynamic social networks with efficiency.