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Node-coupling clustering approaches for link prediction

Li, Fenhua, He, Jing, Huang, Guangyan, Zhang, Yanchun, Shi, Yong and Zhou, Rui 2015, Node-coupling clustering approaches for link prediction, Knowledge-based systems, vol. 89, pp. 669-680, doi: 10.1016/j.knosys.2015.09.014.

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Title Node-coupling clustering approaches for link prediction
Author(s) Li, Fenhua
He, Jing
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Zhang, Yanchun
Shi, Yong
Zhou, Rui
Journal name Knowledge-based systems
Volume number 89
Start page 669
End page 680
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-11
ISSN 0950-7051
Summary Due to the potential important information in real world networks, link prediction has become an interesting focus of different branches of science. Nevertheless, in "big data" era, link prediction faces significant challenges, such as how to predict the massive data efficiently and accurately. In this paper, we propose two novel node-coupling clustering approaches and their extensions for link prediction, which combine the coupling degrees of the common neighbor nodes of a predicted node-pair with cluster geometries of nodes. We then present an experimental evaluation to compare the prediction accuracy and effectiveness between our approaches and the representative existing methods on two synthetic datasets and six real world datasets. The experimental results show our approaches outperform the existing methods.
Language eng
DOI 10.1016/j.knosys.2015.09.014
Field of Research 080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
15 Commerce, Management, Tourism And Services
17 Psychology And Cognitive Sciences
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081391

Document type: Journal Article
Collection: School of Information Technology
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