Efficient identification of node importance in social networks |
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| Institution: | 1. Institute of Computing, Federal University of Amazonas, AM, Brazil;2. Department of Computer Science, Federal University of Minas Gerais, MG, Brazil;3. Institute of Computing, University of Campinas, SP, Brazil;1. Department of Information Management, National Sun Yat-Sen University, No. 70, Lienhai Rd., Kaohsiung 80424, Taiwan;2. School of Information Sciences, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA 15260, USA;1. Aix-Marseille Université, CNRS, Univ. Toulon, ENSAM (LSIS, UMR 7296), France;2. LIMSI, CNRS, Univ. Paris-Sud, Université Paris-Saclay, France;3. LIA, Université d’Avignon, France;4. IRIT UMR5505 CNRS, ESPE UT2J, Université de Toulouse, France;1. Department of Computer Engineering, Do?u? University, Istanbul, Turkey;2. Department of Computer Engineering, Marmara University, Istanbul, Turkey;1. Institute of Computing, Federal University of Amazonas –Av. Gen. Rodrigo Otávio, 3000, Manaus 69077-000, AM, Brazil;2. Neemu S/A, Av. Via Lactea, 1374, Manaus 69060-020, AM, Brazil |
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| Abstract: | In social networks, identifying influential nodes is essential to control the social networks. Identifying influential nodes has been among one of the most intensively studies of analyzing the structure of networks. There are a multitude of evaluation indicators of node importance in social networks, such as degree, betweenness and cumulative nomination and so on. But most of the indicators only reveal one characteristic of the node. In fact, in social networks, node importance is not affected by a single factor, but is affected by a number of factors. Therefore, the paper puts forward a relatively comprehensive and effective method of evaluation node importance in social networks by using the multi-objective decision method. Firstly, we select several different representative indicators given a certain weight. We regard each node as a solution and different indicators of each node as the solution properties. Then through calculating the closeness degree of each node to the ideal solution, we obtain evaluation indicator of node importance in social networks. Finally, we verify the effectiveness of the proposed method experimentally on a few actual social networks. |
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