A novel measure to identify influential nodes in complex networks based on network global efficiency

Abstract

Identifying influential nodes is a basic measure of characterizing the structure and dynamics in complex networks. In this paper, we use network global efficiency by removing edges to propose a new centrality measure for identifying influential nodes in complex networks. Differing from the traditional network global efficiency, the proposed measure is determined by removing edges from networks, not removing nodes. Instead of static structure properties which are exhibited by other traditional centrality measures, such as degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), we focus on the perspective of dynamical process and global structure in complex networks. Susceptible-infected (SI) model is utilized to evaluate the performance of the proposed method. Experimental results show that the proposed measure is more effective than the other three centrality measures.