Finding Influencers in Complex Networks: A Novel Method Based on Information Theory

Key node identification is one of the most fundamental and significant problems in complex network research. This article presents a novel approach called information-theory-based centrality
$C_{H}$
to distinguish influential nodes in complex networks. From an information-theoretic perspective, nodes involved in many information channels will improve the quality of communication. Thus, the greater the number of information channels a node participates, the more the importance of this node during information transmission. With this motivation, the local and global connectivity are measured according to the resistance distance, which captures the characteristic that multiple available routes reduce the resistance of information transmission on networks. We further model the probability of information diffusion according to the local and global connectivity and put forward an information-theory-based centrality to identify influential nodes. Finally, extensive evaluations are implemented on real-world networks to verify the feasibility and effectiveness of the proposed method. According to the results, our proposed method outperforms classical methods in identifying influential nodes and also indicates the potential for analyzing the influence evolution of networks, which shows a positive and effective impact on locating influencers and predicting potential key players in the future.