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• W2542140755 abstract "Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, or contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of Susceptible-Infected-Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets." @default.
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• W2542140755 date "2014-12-01" @default.
• W2542140755 modified "2023-09-23" @default.
• W2542140755 title "Identify Influential Social Network Spreaders" @default.
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• W2542140755 doi "https://doi.org/10.1109/icdmw.2014.31" @default.
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