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• W3186226641 startingPage "2150183" @default.
• W3186226641 abstract "The identification of influential nodes is one of the most significant and challenging research issues in network science. Many centrality indices have been established starting from topological features of networks. In this work, we propose a novel gravity model based on position and neighborhood (GPN), in which the mass of focal and neighbor nodes is redefined by the extended outspreading capability and modified k-shell iteration index, respectively. This new model comprehensively considers the position, local and path information of nodes to identify influential nodes. To test the effectiveness of GPN, a number of simulation experiments on nine real networks have been conducted with the aid of the susceptible–infected–recovered (SIR) model. The results indicate that GPN has better performance than seven popular methods. Furthermore, the proposed method has near linear time cost and thus it is suitable for large-scale networks." @default.
• W3186226641 created "2021-08-02" @default.
• W3186226641 creator A5026540169 @default.
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• W3186226641 date "2021-07-10" @default.
• W3186226641 modified "2023-10-14" @default.
• W3186226641 title "GPN: A novel gravity model based on position and neighborhood to identify influential nodes in complex networks" @default.
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• W3186226641 doi "https://doi.org/10.1142/s0217979221501836" @default.
• W3186226641 hasPublicationYear "2021" @default.
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