Identifying influential spreaders in complex networks based on improved k-shell method

Identifying influential spreaders in complex networks is a fundamental network project. It has drawn great attention in recent years because of its great theoretical significance and practical value in some fields. K-shell is an efficient method for identifying influential spreaders. However, k-shell neglects information about the topological position of the nodes. In this paper, we propose an improved algorithm based on the k-shell and node information entropy named IKS to identify influential spreaders from the higher shell as well as the lower shell. The proposed method employs the susceptible-infected-recovered (SIR) epidemic model, Kendall's coefficient tau, the monotonicity M, and the average shortest path length L-s to evaluate the performance and compare with other benchmark methods. The results of the experiment on eight real-world networks show that the proposed method can rank the influential spreaders more accurately. Moreover, IKS has superior computational complexity and can be extended to large-scale networks. (C) 2020 Published by Elsevier B.V.