Identifying and ranking influential spreaders in complex networks

Abstract

Identifying influential spreaders is an important and fundamental work in control information diffusion. Many methods based on centrality measures such as degree centrality, the betweenness centrality, closeness centrality and eigenvector centrality are proposed in the previous literatures, and it has proved that the shell k decomposition plays overwhelming performance to find influential spreaders in networks. However, as the performance of former three methods is not satisfying enough and shell k decomposition cannot rank nodes in the same core k how to find the influential spreaders is still an open challenge. In this paper, we concerned about the influence of hop neighborhoods on a node and propose a novel metric, shell k values of hop neighborhoods ( NKS ), to estimate the spreading influence of nodes of each shell k in networks. Our experimental resultsshow that the proposed method can quantify the node influence more accurately and provide a more monotonic ranking list than other ranking methods.

Fine-Grained Bandwidth Estimation for Smart Grid Communication Network

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