Detecting opinion leaders in online social networks using HybridRank algorithm

Opinion leaders are those users who have great influence in social networks. It is significant to detect opinion leaders for the study on social networks and other applications. According to the key idea of the PageRank algorithm, a novel algorithm called HybridRank is proposed, taking into account topic-sensitive analysis and temporal characteristics. Our two major contributions are twofold: (1) topic-sensitive analysis is conducted to obtain the clusters in social networks; (2) temporal analysis is proposed to investigate the dynamics of the user's influence over the time. We also provide impressive experimental analysis on a real dataset grabbed from Chinese Sina BBS, showing that the proposed HybridRank Algorithm outperforms various related approaches.