A modified evidential methodology of identifying influential nodes in weighted networks |
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| Authors: | Cai Gao Daijun Wei Yong Hu Sankaran Mahadevan Yong Deng |
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| Institution: | 1. School of Computer and Information Science, Southwest University, Chongqing 400715, China;2. Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Guangzhou 510006, China;3. School of Engineering, Vanderbilt University, TN 37235, USA |
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| Abstract: | How to identify influential nodes in complex networks is still an open hot issue. In the existing evidential centrality (EVC), node degree distribution in complex networks is not taken into consideration. In addition, the global structure information has also been neglected. In this paper, a new Evidential Semi-local Centrality (ESC) is proposed by modifying EVC in two aspects. Firstly, the Basic Probability Assignment (BPA) of degree generated by EVC is modified according to the actual degree distribution, rather than just following uniform distribution. BPA is the generation of probability in order to model uncertainty. Secondly, semi-local centrality combined with modified EVC is extended to be applied in weighted networks. Numerical examples are used to illustrate the efficiency of the proposed method. |
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| Keywords: | Complex networks Influential nodes Weighted network Evidential centrality Dempster&ndash Shafer theory of evidence Semi-local centrality |
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