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Title
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Predicting and recommending collaborations : an author-, institution-, and country-level analysis
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Journal of informetrics. - Amsterdam
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Publication
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Amsterdam
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2014
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ISSN
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1751-1577
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DOI
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10.1016/J.JOI.2014.01.008
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Volume/pages
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8
:2
(2014)
, p. 295-309
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ISI
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000335609900001
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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