Abstract
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.
Highlights
A Community-Based Approach to IdentifyingCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Fast and accurate identification of influential spreaders in a network is essential to the acceleration of information diffusion, inhibition of gossip and spread of a virus
This paper improves on these deficiencies and proposes another index, Community-based Centrality (CbC), which is used to identify the influential spreaders based on the network community structure
Summary
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China. Academic Editors: Guanrong Chen, C.K. Michael Tse, Mustak E.
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