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

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  • Yan, Erjia
  • Guns, Raf

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.

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  • Yan, Erjia & Guns, Raf, 2014. "Predicting and recommending collaborations: An author-, institution-, and country-level analysis," Journal of Informetrics, Elsevier, vol. 8(2), pages 295-309.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:2:p:295-309
    DOI: 10.1016/j.joi.2014.01.008
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    1. Erjia Yan & Cassidy R. Sugimoto, 2011. "Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(8), pages 1498-1514, August.
    2. Hildrun Kretschmer, 2004. "Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the Web," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 409-420, August.
    3. Jarno Hoekman & Koen Frenken & Frank Oort, 2009. "The geography of collaborative knowledge production in Europe," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(3), pages 721-738, September.
    4. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    5. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    6. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    7. Erjia Yan & Cassidy R. Sugimoto, 2011. "Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(8), pages 1498-1514, August.
    8. Roderik Ponds & Frank Van Oort & Koen Frenken, 2007. "The geographical and institutional proximity of research collaboration," Papers in Regional Science, Wiley Blackwell, vol. 86(3), pages 423-443, August.
    9. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    10. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
    11. Guo, Fangjian & Yang, Zimo & Zhou, Tao, 2013. "Predicting link directions via a recursive subgraph-based ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3402-3408.
    12. Dalibor Fiala & François Rousselot & Karel Ježek, 2008. "PageRank for bibliographic networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(1), pages 135-158, July.
    13. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    14. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    15. Birger Larsen & Peter Ingwersen & Berit Lund, 2009. "Data fusion according to the principle of polyrepresentation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(4), pages 646-654, April.
    16. Ron Boschma, 2005. "Proximity and Innovation: A Critical Assessment," Regional Studies, Taylor & Francis Journals, vol. 39(1), pages 61-74.
    17. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    18. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    Full references (including those not matched with items on IDEAS)

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    19. Ahlgren, Per & Waltman, Ludo, 2014. "The correlation between citation-based and expert-based assessments of publication channels: SNIP and SJR vs. Norwegian quality assessments," Journal of Informetrics, Elsevier, vol. 8(4), pages 985-996.
    20. Noémi Gaskó & Rodica Ioana Lung & Mihai Alexandru Suciu, 2016. "A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 613-632, August.
    21. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.

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