Identifying influential nodes in complex networks based on a spreading influence related centrality

Identifying the influential nodes in complex networks is still a significant topic in theoretical and practical recently. Many efficient and practical centrality indices have been proposed on the understanding of network topology features. But the indices still have more or less limitations. Hence, improving the accuracy of centrality indices is an important topic. In the paper, a fusion index named as spreading influence related centrality is proposed to identify the influence of nodes by extracting and synthesizing topology feature information of traditional centrality indices and spreading influence. The simulation experiment of spreading and node removal on four real networks are employed to verify the accuracy of proposed centrality. The experiment shows that the fusion index can provides a more reasonable ranking list than traditional methods. (C) 2019 Published by Elsevier B.V.