Fast ranking influential nodes in complex networks using a k-shell iteration factor

Identifying the influential nodes of complex networks is important for optimizing the network structure or efficiently disseminating information through networks. The k-shell method is a widely used node ranking method that has inherent advantages in performance and efficiency. However, the iteration information produced in k-shell decomposition has been neglected in node ranking. This paper presents a fast ranking method to evaluate the influence capability of nodes using a k-shell iteration factor. The experimental results with respect to monotonicity, correctness and efficiency have demonstrated that the proposed method can yield excellent performance on artificial and real world networks. It discriminates the influence capability of nodes more accurately and provides a more reasonable ranking list than previous methods.