Identifying influential spreaders in large-scale networks based on evidence theory

Identifying the most influential spreaders is an important issue in epidemic spreading, viral marketing, and controlling the spreading process of information. Thus, methods for identifying influential spreaders in complex networks have received increasing attention from researchers. During recent decades, researchers have proposed many methods. However, each of these methods has advantages and disadvantages. In this paper, we propose a new efficient algorithm for identifying influential spreaders based on the Dempster-Shafer (D-S) evidence theory, which is a complete theory that deal with uncertainty or imprecision. We call our proposed algorithm D-2SN, which trades off between the degree (D) and the 2-step neighbor information (2SN) of every node in a network. Specifically, the influence of both the degree and the 2SN of each node are represented by a basic probability assignment (BPA). D-2SN is determined by the fusion of these BPAs. Since the algorithm considers not only the topological structure of each node, but also its neighbors' structure, it is a good choice to balance cost and performance. In addition, it also exhibits very low time complexity O(< k > n), which makes it applicable to large-scale networks. To evaluate the performance of D-2SN, we employ the Independent Cascade (IC) and Liner Threshold (LT) models to examine the spreading efficiency of each node and compare D-2SN with several classic methods in eight real-world networks. Extensive experiments demonstrate the superiority of D-2SN to other baseline methods. (C) 2019 Elsevier B.V. All rights reserved.