Identifying a set of influential spreaders in complex networks

By Jian-Xiong Zhang, Duanbing Chen, Qiang Dong, Zhi Zhao. Scientific Reports, 2016

301 citations recorded by the source corpus.

Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/56382224d7f1aecd7b3d4a2423498970372be13c

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

Vital nodes identification in complex networks (2016, methodology): “It can be seen that Ji’s method and VoteRank have relatively higher ability to find a set of high influential nodes to maximize information coverage than other methods.”