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      Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

      1 , 2 , 3
      Discrete Dynamics in Nature and Society
      Hindawi Limited

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          Abstract

          Identifying influential spreaders in complex networks is crucial for containing virus spread, accelerating information diffusion, and promoting new products. In this paper, inspired by the effect of leaders on social ties, we propose the most influential neighbors’ k -shell index that is the weighted sum of the products between k -core values of itself and the node with the maximum k -shell values. We apply the classical Susceptible-Infected-Recovered (SIR) model to verify the performance of our method. The experimental results on both real and artificial networks show that the proposed method can quantify the node influence more accurately than degree centrality, betweenness centrality, closeness centrality, and k -shell decomposition method.

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          Most cited references31

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            Factoring and weighting approaches to status scores and clique identification

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              Network structure and minimum degree

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                Author and article information

                Journal
                Discrete Dynamics in Nature and Society
                Discrete Dynamics in Nature and Society
                Hindawi Limited
                1026-0226
                1607-887X
                August 23 2018
                August 23 2018
                : 2018
                : 1-6
                Affiliations
                [1 ]Department of Transportation Economics and Logistics Management, College of Economics, Shenzhen University, Nanhai Ave 3688, Shenzhen, Guangdong 518060, China
                [2 ]Department of Risk Management and Insurance, College of Economics, Shenzhen University, Nanhai Ave 3688, Shenzhen, Guangdong 518060, China
                [3 ]China Center for Special Economic Zone Research, Shenzhen University, Nanhai Ave 3688, Shenzhen, Guangdong 518060, China
                Article
                10.1155/2018/3649079
                471f7803-e41c-42aa-b0f5-9f0286301337
                © 2018

                http://creativecommons.org/licenses/by/4.0/

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