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. Attached Files Name Description MIMEType Size Downloads Title Node-coupling clustering approaches for link prediction Author(s) Li, Fenhua He, Jing 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 Collections: School of Information Technology 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 16 Feb 2016, 10:23:35 EST Tue, 24 May 2016, 21:47:30 EST Wed, 25 May 2016, 05:00:37 EST Wed, 25 May 2016, 06:08:03 EST Thu, 26 May 2016, 14:11:32 EST Thu, 26 May 2016, 14:29:30 EST Sat, 14 Apr 2018, 02:32:17 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 8 times in Scopus Search Google Scholar Access Statistics: 162 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 24 May 2016, 21:47:30 EST

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