Title
Predicting and recommending potential research collaborations Predicting and recommending potential research collaborations
Author
Faculty/Department
Faculty of Social Sciences. Instructional and Educational Sciences
Publication type
conferenceObject
Publication
Vienna :Austria Institute of Technology, [*]
Subject
Documentation and information
Source (book)
Proceedings of ISSI 2013 : 14th International Society of Scientometrics and Informetrics Conference Vienna, Austria, 15th to 20th July 2013 / Gorraiz, Juan [edit.]; et al.
ISBN - Hoofdstuk
978-3-200-03135-7
ISI
000353961700111
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
We study research collaborations between cities in Africa, the Middle East and South-Asia, focusing on the topics of malaria and tuberculosis. For this investigation we introduce a method to predict or recommend high-potential future (i.e., not yet realized) collaborations. The proposed method is based on link prediction techniques. A weighted network of co-authorships at the city level is constructed. Next, we calculate scores for each node pair according to three different measures: weighted Katz, rooted PageRank, and SimRank. The resulting scores can be interpreted as indicative of the likelihood of future linkage for the given node pair. A high score for two nodes that are not linked in the network is then treated as a recommendation for future collaboration. Results suggest that of the three measures studied the weighted Katz method leads to the most accurate predictions. Cities that often take part in new intercity collaborations are referred to as facilitator cities.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000353961700111&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000353961700111&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000353961700111&DestLinkType=CitingArticles&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
Handle