A modified evidential methodology of identifying influential nodes in weighted networks

Cai Gao, Daijun Wei, Yong Hu, Sankaran Mahadevan and Yong Deng

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 21, 5490-5500

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.

Keywords: Complex networks; Influential nodes; Weighted network; Evidential centrality; Dempster–Shafer theory of evidence; Semi-local centrality (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:21:p:5490-5500

DOI: 10.1016/j.physa.2013.06.059

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