Identifying influential nodes for the networks with community structure

Abstract Identifying influential nodes in complex network with community structures attracts lots of attention recently. In this paper, by combining the community structure identification method and the closeness of the network, we present a method for identifying the influential nodes. Comparing with the Susceptible-Infected-Recovered (SIR) model results on synthetic and empirical networks, the accuracy results measured by kendall’s tau τ and spearman ρ indicate that our method outperforms the existing methods to identify influential nodes for networks. The largest improved ratios η for the kendall’s tau τ and spearman ρ reach 161.6% and 157.6%.