A new measure of identifying influential nodes: Efficiency centrality

Abstract

Identifying the influential nodes in complex networks is a fundamental and practical topic at the moment. Local metric like degree centrality measure is relatively simple and of less effectiveness, although the global metrics such as closeness and betweenness centrality measure can better identify influential nodes, still, there are some disadvantages and limitations. In this paper, a new efficiency centrality (EffC) to rank the spreaders in the whole network is proposed, which identify influential nodes by removing each node and meanwhile considering the changing degree of the whole network efficiency after removal. To evaluate the performance of our method, Susceptible-Infected-Recovered (SIR) model is used to simulate the epidemic spreading in four real networks. The experimental and simulated results show the efficiency and practicability of the proposed method. Thus, it is significant to rank spreaders in complex networks by using Network Efficiency. And our proposed EffC is proved to be a feasible and effective measure to identify influential nodes.