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Predicting the impact and publication date of individual scientists’ future papers

Author

Listed:
  • Yuhao Zhou

    (University of Fribourg)

  • Ruijie Wang

    (University of Fribourg)

  • An Zeng

    (Beijing Normal University)

Abstract

Predicting the future career of individual scientists is an important yet challenging problem with numerous applications such as recruitment of scientific research positions, promoting outstanding academic staff, and managing scientific grant proposals. Despite that much effort has been devoted to predict scientists’ future performance and success, yet these works focus on the macro future performance of scholars from the perspective of their career ages. A related but different task is to predict the impact and publication date of each future paper. We regard this micro level prediction problem as a dynamic series auto-regression task, and a deep learning method is designed to solve it. The experiments show that our method outperforms the state-of-the-art method in this issue.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:4:d:10.1007_s11192-022-04286-w
    DOI: 10.1007/s11192-022-04286-w
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    References listed on IDEAS

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    1. Batista-Jr, Antônio de Abreu & Gouveia, Fábio Castro & Mena-Chalco, Jesús P., 2021. "Predicting the Q of junior researchers using data from the first years of publication," Journal of Informetrics, Elsevier, vol. 15(2).
    2. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
    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. Tobias Mistele & Tom Price & Sabine Hossenfelder, 2019. "Predicting authors’ citation counts and h-indices with a neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 87-104, July.
    5. Christopher McCarty & James W. Jawitz & Allison Hopkins & Alex Goldman, 2013. "Predicting author h-index using characteristics of the co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 467-483, August.
    6. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).
    7. 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.
    8. Lu Liu & Yang Wang & Roberta Sinatra & C. Lee Giles & Chaoming Song & Dashun Wang, 2018. "Hot streaks in artistic, cultural, and scientific careers," Nature, Nature, vol. 559(7714), pages 396-399, July.
    9. Mengjiao Qi & An Zeng & Menghui Li & Ying Fan & Zengru Di, 2017. "Standing on the shoulders of giants: the effect of outstanding scientists on young collaborators’ careers," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1839-1850, June.
    10. Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
    11. Lawrence D. Fu & Constantin F. Aliferis, 2010. "Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 257-270, October.
    12. 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).
    13. Zhang, Fang & Wu, Shengli, 2020. "Predicting future influence of papers, researchers, and venues in a dynamic academic network," Journal of Informetrics, Elsevier, vol. 14(2).
    14. 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.
    15. Daniel E. Acuna & Stefano Allesina & Konrad P. Kording, 2012. "Predicting scientific success," Nature, Nature, vol. 489(7415), pages 201-202, September.
    16. Wenjie Lu & Jiazheng Li & Yifan Li & Aijun Sun & Jingyang Wang, 2020. "A CNN-LSTM-Based Model to Forecast Stock Prices," Complexity, Hindawi, vol. 2020, pages 1-10, November.
    17. Amin Mazloumian, 2012. "Predicting Scholars' Scientific Impact," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-5, November.
    18. Xing, Yanmeng & Wang, Fenghua & Zeng, An & Ying, Fan, 2021. "Solving the cold-start problem in scientific credit allocation," Journal of Informetrics, Elsevier, vol. 15(3).
    19. Jianlin Zhou & An Zeng & Ying Fan & Zengru Di, 2016. "Ranking scientific publications with similarity-preferential mechanism," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 805-816, February.
    20. Cao, Xuanyu & Chen, Yan & Ray Liu, K.J., 2016. "A data analytic approach to quantifying scientific impact," Journal of Informetrics, Elsevier, vol. 10(2), pages 471-484.
    21. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    22. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    23. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.
    24. Peng Bao & Chengxiang Zhai, 2017. "Dynamic credit allocation in scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 595-606, July.
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