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• W2548124216 abstract "The problem of identifying influential nodes in complex networks has attracted much attention owing to its wide applications, including how to maximize the information diffusion, boost product promotion in a viral marketing campaign, prevent a large scale epidemic and so on. From spreading viewpoint, the probability of one node propagating its information to one other node is closely related to the shortest distance between them, the number of shortest paths and the transmission rate. However, it is difficult to obtain the values of transmission rates for different cases, to overcome such a difficulty, we use the reciprocal of average degree to approximate the transmission rate. Then a semi-local centrality index is proposed to incorporate the shortest distance, the number of shortest paths and the reciprocal of average degree simultaneously. By implementing simulations in real networks as well as synthetic networks, we verify that our proposed centrality can outperform well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, k-shell centrality, and nonbacktracking centrality. In particular, our findings indicate that the performance of our method is the most significant when the transmission rate nears to the epidemic threshold, which is the most meaningful region for the identification of influential nodes." @default.
• W2548124216 created "2016-11-11" @default.
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• W2548124216 date "2017-02-01" @default.
• W2548124216 modified "2023-10-14" @default.
• W2548124216 title "Identification of influential nodes in complex networks: Method from spreading probability viewpoint" @default.
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• W2548124216 doi "https://doi.org/10.1016/j.physa.2016.10.086" @default.
• W2548124216 hasPublicationYear "2017" @default.
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