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

Abstract

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 τ, the monotonicity M, and the average shortest path length Ls 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.