Journal
JOURNAL OF INFORMETRICS
Volume 8, Issue 2, Pages 295-309Publisher
ELSEVIER
DOI: 10.1016/j.joi.2014.01.008
Keywords
Collaboration; Link prediction; Coauthors hip; Networks; Dynamics
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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. (C) 2014 Elsevier Ltd. All rights reserved.
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