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Open AccessArticle

A Community-Based Approach to Identifying Influential Spreaders

College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
Software College, Northeastern University, Shenyang 110819, China
Author to whom correspondence should be addressed.
Academic Editors: Guanrong Chen, C.K. Michael Tse, Mustak E. Yalcin, Hai Yu and Mattia Frasca
Entropy 2015, 17(4), 2228-2252;
Received: 18 December 2014 / Accepted: 1 April 2015 / Published: 14 April 2015
(This article belongs to the Special Issue Recent Advances in Chaos Theory and Complex Networks)
Identifying influential spreaders in complex networks has a significant impact on understanding and control of spreading process in networks. In this paper, we introduce a new centrality index to identify influential spreaders in a network based on the community structure of the network. The community-based centrality (CbC) considers both the number and sizes of communities that are directly linked by a node. We discuss correlations between CbC and other classical centrality indices. Based on simulations of the single source of infection with the Susceptible-Infected-Recovered (SIR) model, we find that CbC can help to identify some critical influential nodes that other indices cannot find. We also investigate the stability of CbC. View Full-Text
Keywords: influential spreaders; community structure; complex networks influential spreaders; community structure; complex networks
MDPI and ACS Style

Zhao, Z.; Wang, X.; Zhang, W.; Zhu, Z. A Community-Based Approach to Identifying Influential Spreaders. Entropy 2015, 17, 2228-2252.

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