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Identifying prize-winning scientists by a competition-aware ranking

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  • Zhou, Yuhao
  • Wang, Ruijie
  • Zeng, An
  • Zhang, Yi-Cheng

Abstract

Evaluating scholars’ achievements is an important problem in the science of science with applications in the evaluation of grant proposals and promotion applications. Since the number of scholars and the number of scholarly outputs grow exponentially with time, well-designed ranking metrics that have the potential to assist in these tasks are of prime importance. To rank scholars, it is important to put their achievements in perspective by comparing them with the achievements of other scholars active in the same period. We propose here a particular way of doing so: by computing the evaluated scholar's share on each year's citations which quantifies how the scholar fares in competition with the others. We assess the resulting ranking method using the American Physical Society citation data and four prestigious physics awards. Our results show that the new method significantly outperforms other ranking methods in identifying the prize laureates.

Suggested Citation

  • Zhou, Yuhao & Wang, Ruijie & Zeng, An & Zhang, Yi-Cheng, 2020. "Identifying prize-winning scientists by a competition-aware ranking," Journal of Informetrics, Elsevier, vol. 14(3).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:3:s1751157719304766
    DOI: 10.1016/j.joi.2020.101038
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    References listed on IDEAS

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    1. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    2. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    3. Mariani, Manuel Sebastian & Medo, Matúš & Zhang, Yi-Cheng, 2016. "Identification of milestone papers through time-balanced network centrality," Journal of Informetrics, Elsevier, vol. 10(4), pages 1207-1223.
    4. Abrishami, Ali & Aliakbary, Sadegh, 2019. "Predicting citation counts based on deep neural network learning techniques," Journal of Informetrics, Elsevier, vol. 13(2), pages 485-499.
    5. 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.
    6. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    7. Ying Ding & Erjia Yan & Arthur Frazho & James Caverlee, 2009. "PageRank for ranking authors in co‐citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2229-2243, November.
    8. Robert P. Light & David E. Polley & Katy Börner, 2014. "Open data and open code for big science of science studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1535-1551, November.
    9. Fiala, Dalibor, 2012. "Time-aware PageRank for bibliographic networks," Journal of Informetrics, Elsevier, vol. 6(3), pages 370-388.
    10. 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.
    11. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    12. G. Van Hooydonk, 1997. "Fractional counting of multiauthored publications: Consequences for the impact of authors," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 48(10), pages 944-945, October.
    13. Lutz Bornmann & Hans-Dieter Daniel, 2005. "Does the h-index for ranking of scientists really work?," Scientometrics, Springer;Akadémiai Kiadó, vol. 65(3), pages 391-392, December.
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    4. Zhao, Zhi-Dan & Chen, Jiahao & Lu, Yichuan & Zhao, Na & Jiang, Dazhi & Wang, Bing-Hong, 2021. "Dynamic patterns of open review process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
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    6. Jingda Ding & Yifan Chen & Chao Liu, 2023. "Exploring the research features of Nobel laureates in Physics based on the semantic similarity measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5247-5275, September.
    7. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
    8. Bilal Barış Alkan & Leyla Karakuş & Bekir Direkci, 2023. "Knowledge discovery from the texts of Nobel Prize winners in literature: sentiment analysis and Latent Dirichlet Allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5311-5334, September.

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