Edge-weights-based method to identify influential spreaders in complex networks

Abstract

Identifying influential nodes has drawn great attention in recent years. In this paper, a novel method for identifying influential spreaders based on potential edge weights (WDK for simplicity) for both undirected and unweighted networks is proposed. Degree and k-shell of a node and its neighbors are considered simultaneously, which are regarded as the weight of the edge directly connected to the node. The algorithm considers not only the local information of nodes but also their location information. The proposed method not only improves the accuracy of node mining but also has approximately linear time complexity, which indicates that the proposed method is suitable for large-scale networks. In order to validate the effectiveness of the proposed method, different evaluation indexes are introduced in nine real networks. Compared with five classical key nodes identification methods, the experimental results show that the proposed method performs optimally in all networks.