A new approach to identify influential spreaders in complex networks

Abstract

In the research of the propagation model of complex network, it is of theoretical and practical significance to detect the most influential spreaders. Global metrics such as degree centrality, closeness centrality, betweenness centrality and K-shell centrality can be used to identify the influential spreaders. These approaches are simple but have low accuracy. We propose K-shell and Community centrality (KSC) model. This model considers not only the internal properties of nodes but also the external properties of nodes, such as the com-munity which these nodes belong to. The Susceptible-Infected-Recovered (SIR) model is used to evaluate the performance of KSC model. The experiment result shows that our method is better to identify the most influential nodes. This paper comes up with a new idea and method for the study in this field. © 2013 Springer-Verlag Berlin Heidelberg.