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Scientometrics

, Volume 119, Issue 2, pp 687–706 | Cite as

Formational bounds of link prediction in collaboration networks

  • Jinseok KimEmail author
  • Jana Diesner
Article

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.

Keywords

Collaboration network Link prediction Network evolution Link formation primitives Preferential attachment 

Notes

Acknowledgements

This work is supported, in part, by Korea Institute of Science and Technology Information (KISTI). We would like to thank Vetle Torvik (University of Illinois at Urbana-Champaign), the American Physical Society, DBLP, and KISTI for providing datasets. We are also grateful to Mark E. J. Newman (University of Michigan) for providing code for disambiguating author names in APS data and Raf Guns (University of Antwerp) for comments on link prediction processes in LinkPred.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Institute for Research on Innovation and Science, Survey Research Center, Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  2. 2.School of Information SciencesUniversity of Illinois at Urbana-ChampaignChampaignUSA

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