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Open AccessArticle

A Community-Based Approach to Identifying Influential Spreaders

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College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
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Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
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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; https://doi.org/10.3390/e17042228
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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