Journal article
Predicting and recommending collaborations: An author-, institution-, and country-level analysis
Journal of informetrics, v 8(2), pp 295-309
Apr 2014
Abstract
•We study dynamic aspect of scientific collaborations at author-, institution-, and country-levels.•Eight link predictors are applied and evaluated.•For the employed data set, denser networks yield more precise collaboration predictions.•Neighbor-information-based predictors produce more similar outcomes than topology-based ones.•Author-, institution-, and country-level collaborations are recommended.
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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Details
- Title
- Predicting and recommending collaborations: An author-, institution-, and country-level analysis
- Creators
- Erjia Yan - College of Computing and Informatics, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USARaf Guns - University of Antwerp, Institute of Education and Information Sciences, IBW, Venusstraat 35, 2000 Antwerpen, Belgium
- Publication Details
- Journal of informetrics, v 8(2), pp 295-309
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000335609900001
- Scopus ID
- 2-s2.0-84893498151
- Other Identifier
- 991014976819704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Information Science & Library Science