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Formational bounds of link prediction in collaboration networks

Author

Listed:
  • Jinseok Kim

    (University of Michigan)

  • Jana Diesner

    (University of Illinois at Urbana-Champaign)

Abstract

Link prediction in collaboration networks is often solved by identifying structural properties of existing nodes that are disconnected at one point in time, and that share a link later on. The maximally possible recall rate or upper bound of this approach’s success is capped by the proportion of links that are formed among existing nodes embedded in these properties. Consequentially, sustained links as well as links that involve one or two new network participants are typically not predicted. The purpose of this study is to highlight formational constraints that need to be considered to increase the practical value of link prediction methods targeted for collaboration networks. In this study, we identify the distribution of basic link formation types based on four large-scale, over-time collaboration networks, showing that roughly speaking, 25% of links represent continued collaborations, 25% of links are new collaborations between existing authors, and 50% are formed between an existing author and a new network member. This implies that for collaboration networks, increasing the accuracy of computational link prediction solutions may not be a reasonable goal when the ratio of collaboration links that are eligible to the classic link prediction process is low.

Suggested Citation

  • Jinseok Kim & Jana Diesner, 2019. "Formational bounds of link prediction in collaboration networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 687-706, May.
  • Handle: RePEc:spr:scient:v:119:y:2019:i:2:d:10.1007_s11192-019-03055-6
    DOI: 10.1007/s11192-019-03055-6
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    References listed on IDEAS

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    1. Kim, Jinseok & Diesner, Jana, 2015. "The effect of data pre-processing on understanding the evolution of collaboration networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 226-236.
    2. Staša Milojević, 2010. "Modes of collaboration in modern science: Beyond power laws and preferential attachment," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1410-1423, July.
    3. Jinseok Kim & Liang Tao & Seok-Hyoung Lee & Jana Diesner, 2016. "Evolution and structure of scientific co-publishing network in Korea between 1948–2011," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 27-41, April.
    4. Brent D Fegley & Vetle I Torvik, 2013. "Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    5. Staša Milojević, 2010. "Modes of collaboration in modern science: Beyond power laws and preferential attachment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(7), pages 1410-1423, July.
    6. 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.
    7. Wagner, Caroline S. & Leydesdorff, Loet, 2005. "Network structure, self-organization, and the growth of international collaboration in science," Research Policy, Elsevier, vol. 34(10), pages 1608-1618, December.
    8. Raf Guns & Ronald Rousseau, 2014. "Recommending research collaborations using link prediction and random forest classifiers," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1461-1473, November.
    9. Tibor Braun & Wolfgang Glänzel & András Schubert, 2001. "Publication and cooperation patterns of the authors of neuroscience journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(3), pages 499-510, July.
    10. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    11. Tibor Braun & Wolfgang Glänzel & András Schubert, 2001. "Publication and cooperation patterns of the authors of neuroscience journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 499-510, January.
    12. Guillaume Cabanac & Gilles Hubert & Béatrice Milard, 2015. "Academic careers in Computer Science: continuance and transience of lifetime co-authorships," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 135-150, January.
    13. 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.
    14. Jinseok Kim, 2018. "Evaluating author name disambiguation for digital libraries: a case of DBLP," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1867-1886, September.
    15. Jinseok Kim & Jana Diesner, 2016. "Distortive effects of initial-based name disambiguation on measurements of large-scale coauthorship networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(6), pages 1446-1461, June.
    16. 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.
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