A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks

Cai Gao, Xin Lan, Xiaoge Zhang, Yong Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

60 Citations (Scopus)

Abstract

How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.

Original languageEnglish
Article numbere66732
JournalPLoS ONE
Volume8
Issue number6
DOIs
Publication statusPublished - 14 Jun 2013
Externally publishedYes

ASJC Scopus subject areas

  • General

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