Link prediction in co-authorship networks based on hybrid content similarity metric

Chuan, Pham Minh and Son, Le Hoang and Ali, Mumtaz and Khang, Tran Dinh and Huong, Le Thanh and Dey, Nilanjan (2017) Link prediction in co-authorship networks based on hybrid content similarity metric. Applied Intelligence. pp. 1-17. ISSN 0924-669X

Abstract

Link prediction in online social networks is used to determine new interactions among its members which are likely to occur in the future. Link prediction in the coauthorship network has been regarded as one of the main targets in link prediction researches so far. Researchers have focused on analyzing and proposing solutions to give efficient recommendation for authors who can work together in a science project. In order to give precise prediction of links between two ubiquitous authors in a co-authorship network, it is preferable to design a similarity metric between them and then utilizing it to determine the most possible co-author(s). However, the relevant researches did not regard the integration of paper’s content in the metric itself. This is important when considering the collaboration between scientists since it is possible that authors having same research interests are more likely to have a joint paper than those in different researches. In this paper, we propose a new metric for link prediction in the coauthorship network based on the content similarity named as LDAcosin. Mathematical notions of the link prediction in the co-authorship network and a link prediction algorithm based on topic modeling are proposed. The new metric is experimentally validated on the public bibliographic collection.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online 05/12/2017. Permanent restricted access to Article/First version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 05 Feb 2018 01:24
Last Modified: 08 May 2018 01:53
Uncontrolled Keywords: link prediction, co-authorship networks, network topology, LDA, topic modeling
Fields of Research : 01 Mathematical Sciences > 0199 Other Mathematical Sciences > 019999 Mathematical Sciences not elsewhere classified
01 Mathematical Sciences > 0102 Applied Mathematics > 010201 Approximation Theory and Asymptotic Methods
Identification Number or DOI: doi:007/s10489-017-1086-x
URI: http://eprints.usq.edu.au/id/eprint/33645

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