Ranking influential nodes in complex networks based on local and global structures

Abstract

Identifying influential nodes in complex networks is an open and challenging issue. Many measures have been proposed to evaluate the influence of nodes and improve the accuracy of measuring influential nodes. In this paper, a new method is proposed to identify and rank the influential nodes in complex networks. The proposed method determines the influence of a node based on its local location and global location. It considers both the local and global structure of the network. Traditional degree centrality is improved and combined with the notion of the local clustering coefficient to measure the local influence of nodes, and the classical k-shell decomposition method is improved to measure the global influence of nodes. To evaluate the performance of the proposed method, the susceptible-infected-recovered (SIR) model is utilized to examine the spreading capability of nodes. A number of experiments are conducted on 11 real-world networks to compare the proposed method with other methods. The experimental results show that the proposed method can identify the influential nodes more accurately than other methods.