Measuring ISP topologies with rocketfuel
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
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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.