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Diversity of temporal influence in popularity prediction of scientific publications

Author

Listed:
  • Yanbo Zhou

    (Zhejiang University of Technology)

  • Hongbing Cheng

    (Zhejiang University of Technology)

  • Qu Li

    (Zhejiang University of Technology)

  • Weihong Wang

    (Zhejiang University of Technology)

Abstract

Predicting the future influential papers is a challenging issue witch has attracted many attentions. In this paper, we focused on the temporal information of citations to study the popularity prediction problem from the perspective of citation dynamics. The experimental study of the APS citation data shows that the temporal decay rate of the influence of citations is decay with paper’s age, and the decay rate is a power-law distribution. We introduced the diversity of temporal decay rate of the influence of citations to predict the future popularity of papers, and proposed a diverse temporal decay method. The result shows that this method can improve the prediction accuracy compared with other popularity-based prediction methods. More importantly, this method can detect some of the newly published papers that haven’t accumulated many citations but will quickly become popular in the future.

Suggested Citation

  • Yanbo Zhou & Hongbing Cheng & Qu Li & Weihong Wang, 2020. "Diversity of temporal influence in popularity prediction of scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 383-392, April.
    • Handle: RePEc:spr:scient:v:123:y:2020:i:1:d:10.1007_s11192-020-03354-3
    DOI: 10.1007/s11192-020-03354-3
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    References listed on IDEAS

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    1. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    2. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    3. Yanbo Zhou & An Zeng & Wei-Hong Wang, 2015. "Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-10, March.
    4. Parolo, Pietro Della Briotta & Pan, Raj Kumar & Ghosh, Rumi & Huberman, Bernardo A. & Kaski, Kimmo & Fortunato, Santo, 2015. "Attention decay in science," Journal of Informetrics, Elsevier, vol. 9(4), pages 734-745.
    5. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
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    Cited by:

    1. Yanbo Zhou & Xin-Li Xu & Xu-Hua Yang & Qu Li, 2022. "The influence of disruption on evaluating the scientific significance of papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5931-5945, October.
    2. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    3. Zhou, Yanbo & Li, Qu & Yang, Xuhua & Cheng, Hongbing, 2021. "Predicting the popularity of scientific publications by an age-based diffusion model," Journal of Informetrics, Elsevier, vol. 15(4).

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