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PageRank-based prediction of award-winning researchers and the impact of citations

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  • Fiala, Dalibor
  • Tutoky, Gabriel

Abstract

In this article some recent disputes about the usefulness of PageRank-based methods for the task of identifying influential researchers in citation networks are discussed. In particular, it focuses on the performance of these methods in relation to simple citation counts. With the aim of comparing these two classes of ranking methods, we analyze a large citation network of authors based on almost two million computer science papers and apply four PageRank-based and citations-based techniques to rank authors by importance throughout the period 1990–2014 on a yearly basis. We use ACM SIGMOD E. F. Codd Innovations Award and ACM A. M. Turing Award winners in our baseline lists of outstanding scientists and define four relevance weighting schemes with some predictive power for the ranking methods to increase the relevance of researchers winning in the future. We conclude that citations-based rankings perform better for Codd Award winners, but PageRank-based methods do so for Turing Award recipients when using absolute ranks and PageRank-based rankings outperform the citations-based techniques for both Codd and Turing Award laureates when relative ranks are considered. However, the two ranking groups show smaller differences if more weight is assigned to the relevance of future awardees.

Suggested Citation

  • Fiala, Dalibor & Tutoky, Gabriel, 2017. "PageRank-based prediction of award-winning researchers and the impact of citations," Journal of Informetrics, Elsevier, vol. 11(4), pages 1044-1068.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:4:p:1044-1068
    DOI: 10.1016/j.joi.2017.09.008
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    Citations

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    Cited by:

    1. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "Globalised vs averaged: Bias and ranking performance on the author level," Journal of Informetrics, Elsevier, vol. 13(1), pages 299-313.
    2. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2018. "How to evaluate rankings of academic entities using test data," Journal of Informetrics, Elsevier, vol. 12(3), pages 631-655.
    3. Yanan Wang & An Zeng & Ying Fan & Zengru Di, 2019. "Ranking scientific publications considering the aging characteristics of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 155-166, July.
    4. Bin Wang & Feng Wu & Lukui Shi, 2023. "AGSTA-NET: adaptive graph spatiotemporal attention network for citation count prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 511-541, January.
    5. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2018. "Author ranking evaluation at scale," Journal of Informetrics, Elsevier, vol. 12(3), pages 679-702.
    6. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Zhu, Wanying & Jin, Ching & Ma, Yifang & Xu, Cong, 2023. "Earlier recognition of scientific excellence enhances future achievements and promotes persistence," Journal of Informetrics, Elsevier, vol. 17(2).
    8. Belussi, Fiorenza & Orsi, Luigi & Savarese, Maria, 2019. "Mapping Business Model Research: A Document Bibliometric Analysis," Scandinavian Journal of Management, Elsevier, vol. 35(3).

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