Predicting and recommending collaborations: An author-, institution-, and country-level analysis

Erjia Yan and Raf Guns

Journal of Informetrics, 2014, vol. 8, issue 2, 295-309

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.

Keywords: Collaboration; Link prediction; Coauthorship; Networks; Dynamics (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:8:y:2014:i:2:p:295-309

DOI: 10.1016/j.joi.2014.01.008

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