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• W2018565574 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." @default.
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• W2018565574 date "2015-04-14" @default.
• W2018565574 modified "2023-10-15" @default.
• W2018565574 title "A Community-Based Approach to Identifying Influential Spreaders" @default.
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• W2018565574 doi "https://doi.org/10.3390/e17042228" @default.
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