Publication
Title
Predicting and recommending collaborations : an author-, institution-, and country-level analysis
Author
Abstract
This study examines collaboration dynamics with the goal to predict and recommend collaborations starting from the current topology. Author-, institution-, and country-level collaboration networks are constructed using a ten-year data set on library and information science publications. Different statistical approaches are applied to these collaboration networks. The study shows that, for the employed data set in particular, higher-level collaboration networks (i.e., country-level collaboration networks) tend to yield more accurate prediction outcomes than lower-level ones (i.e., institution- and author-level collaboration networks). Based on the recommended collaborations of the data set, this study finds that neighbor-information-based approaches are more clustered on a 2-D multidimensional scaling map than topology-based ones. Limitations of the applied approaches on sparse collaboration networks are also discussed.
Language
English
Source (journal)
Journal of informetrics. - Amsterdam
Publication
Amsterdam : 2014
ISSN
1751-1577
Volume/pages
8:2(2014), p. 295-309
ISI
000335609900001
Full text (Publishers DOI)
Full text (open access)
Full text (publishers version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 19.02.2014
Last edited 01.04.2017
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