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Identifying social influence in complex networks: a novel conductance eigenvector centrality model

Li, Xujun, Liu, Yezheng, Jiang, Yuanchun and Liu, Xiao 2016, Identifying social influence in complex networks: a novel conductance eigenvector centrality model, Neurocomputing, vol. 210, Special issue: Behavior analysis In SN, pp. 141-154, doi: 10.1016/j.neucom.2015.11.123.

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Title Identifying social influence in complex networks: a novel conductance eigenvector centrality model
Author(s) Li, Xujun
Liu, Yezheng
Jiang, Yuanchun
Liu, Xiao
Journal name Neurocomputing
Volume number 210
Season Special issue: Behavior analysis In SN
Start page 141
End page 154
Total pages 14
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-10-19
ISSN 0925-2312
1872-8286
Keyword(s) influence identification
conductance network
conductance eigenvector centrality
random walk
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
IDENTIFICATION
DISTANCE
RANKING
NODES
Summary Identifying influential peers is an important issue for business to promote commercial strategies in social networks. This paper proposes a conductance eigenvector centrality (CEC) model to measure peer influence in the complex social network. The CEC model considers the social network as a conductance network and constructs methods to calculate the conductance matrix of the network. By a novel random walk mechanism, the CEC model obtains stable CEC values which measure the peer influence in the network. The experiments show that the CEC model can achieve robust performance in identifying peer influence. It outperforms the benchmark algorithms and obtains excellent outcomes when the network has high clustering coefficient.
Language eng
DOI 10.1016/j.neucom.2015.11.123
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089348

Document type: Journal Article
Collection: School of Information Technology
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Created: Thu, 24 Nov 2016, 16:42:04 EST

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