A modified evidential methodology of identifying influential nodes in weighted networks

被引：122

作者：

Gao, Cai ^{[1
]}

Wei, Daijun ^{[1
]}

Hu, Yong ^{[2
]}

Mahadevan, Sankaran ^{[3
]}

Deng, Yong ^{[1
,3
]}

机构：

[1] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China

[2] Guangdong Univ Foreign Studies, Inst Business Intelligence & Knowledge Discovery, Guangzhou 510006, Guangdong, Peoples R China

[3] Vanderbilt Univ, Sch Engn, Nashville, TN 37235 USA

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2013年 / 392卷 / 21期

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

Complex networks; Influential nodes; Weighted network; Evidential centrality; Dempster-Shafer theory of evidence; Semi-local centrality; COMPLEX NETWORKS; LINK PREDICTION; EPIDEMIC SPREAD; LARGE-SCALE; CENTRALITY;

D O I：

10.1016/j.physa.2013.06.059

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：5490 / 5500

页数：11

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