Identifying influential nodes in complex networks |
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Authors: | Duanbing ChenLinyuan Lü Ming-Sheng ShangYi-Cheng Zhang Tao Zhou |
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Affiliation: | a Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of Chinab Physics Department, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerlandc Department of Modern Physics, University of Science and Technology of China, Hefei 230026, People’s Republic of China |
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Abstract: | Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible-Infected-Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes. |
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Keywords: | Complex networks Centrality measures Influential nodes Spreading SIR model |
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