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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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