Identifying influential nodes in weighted networks based on evidence theory

Daijun Wei, Xinyang Deng, Xiaoge Zhang, Yong Deng, Sankaran Mahadevan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

183 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2564-2575
Number of pages12
JournalPhysica A: Statistical Mechanics and its Applications
Volume392
Issue number10
DOIs
Publication statusPublished - 15 May 2013
Externally publishedYes

Keywords

  • Complex networks
  • Dempster-Shafer theory of evidence
  • Influential nodes
  • Weighted network

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

Fingerprint

Dive into the research topics of 'Identifying influential nodes in weighted networks based on evidence theory'. Together they form a unique fingerprint.

Cite this