Identifying influential nodes in weighted networks based on evidence theory

被引：191

作者：

Wei, Daijun ^{[1
,2
]}

Deng, Xinyang ^{[1
]}

Zhang, Xiaoge ^{[1
]}

Deng, Yong ^{[1
,3
]}

Mahadevan, Sankaran ^{[3
]}

机构：

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

[2] Hubei Univ Nationalities, Sch Sci, Enshi 445000, 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 / 10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; Dempster-Shafer theory of evidence; COMPLEX NETWORKS; EPIDEMIC SPREAD; LARGE-SCALE; CENTRALITY;

D O I：

10.1016/j.physa.2013.01.054

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

The design of an effective ranking method to identify influential nodes is an important problem in the study of complex networks. In this paper, a new centrality measure is proposed based on the Dempster-Shafer evidence theory. The proposed measure trades off between the degree and strength of every node in a weighted network. The influences of both the degree and the strength of each node are represented by basic probability assignment (BPA). The proposed centrality measure is determined by the combination of these BPAs. Numerical examples are used to illustrate the effectiveness of the proposed method. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：2564 / 2575

页数：12

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