Identifying and ranking influential spreaders in complex networks by neighborhood coreness

Identifying influential spreaders is an important issue in understanding the dynamics of information diffusion in complex networks. The k-shell index, which is the topological location of a node in a network, is a more efficient measure at capturing the spreading ability of a node than are the degree and betweenness centralities. However, the k-shell decomposition fails to yield the monotonic ranking of spreaders because it assigns too many nodes with the same k-shell index. In this paper, we propose a novel measure, coreness centrality, to estimate the spreading influence of a node in a network using the k-shell indices of its neighbors. Our experimental results on both real and artificial networks, compared with an epidemic spreading model, show that the proposed method can quantify the node influence more accurately and provide a more monotonic ranking list than other ranking methods.