Title
Recommending research collaborations using link prediction and random forest classifiersRecommending research collaborations using link prediction and random forest classifiers
Author
Faculty/Department
Faculty of Social Sciences. Instructional and Educational Sciences
Research group
Department Instructional and Educational Sciences - other
Book, Library and Information
Publication type
article
Publication
Amsterdam,
Subject
Documentation and information
Mathematics
Computer. Automation
Source (journal)
Scientometrics: an international journal for all quantitative aspects of the science of science and science policy. - Amsterdam
Scientometrics: an international journal for all quantitative aspects of the science of science and science policy. - Amsterdam
Volume/pages
101(2014):2, p. 1461-1473
ISSN
0138-9130
ISI
000343609900034
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
We introduce a method to predict or recommend high-potential future (i.e., not yet realized) collaborations. The proposed method is based on a combination of link prediction and machine learning techniques. First, a weighted co-authorship network is constructed. We calculate scores for each node pair according to different measures called predictors. The resulting scores can be interpreted as indicative of the likelihood of future linkage for the given node pair. To determine the relative merit of each predictor, we train a random forest classifier on older data. The same classifier can then generate predictions for newer data. The top predictions are treated as recommendations for future collaboration. We apply the technique to research collaborations between cities in Africa, the Middle East and South-Asia, focusing on the topics of malaria and tuberculosis. Results show that the method yields accurate recommendations. Moreover, the method can be used to determine the relative strengths of each predictor.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/70d189/f51123c3.pdf
E-info
https://repository.uantwerpen.be/docman/iruaauth/d9c57c/966b89e8060.pdf
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000343609900034&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000343609900034&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000343609900034&DestLinkType=CitingArticles&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
Handle