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The aging effect in evolving scientific citation networks

Author

Listed:
  • Feng Hu

    (Qinghai Normal University
    Key Laboratory of Tibetan information processing, Ministry of Education
    Tibetan information processing and Machine Translation Key Laboratory of Qinghai Province)

  • Lin Ma

    (Alibaba Research Center for Complexity Sciences, Hangzhou Normal University)

  • Xiu-Xiu Zhan

    (Alibaba Research Center for Complexity Sciences, Hangzhou Normal University
    Delft University of Technology)

  • Yinzuo Zhou

    (Alibaba Research Center for Complexity Sciences, Hangzhou Normal University)

  • Chuang Liu

    (Alibaba Research Center for Complexity Sciences, Hangzhou Normal University)

  • Haixing Zhao

    (Qinghai Normal University
    Key Laboratory of Tibetan information processing, Ministry of Education
    Tibetan information processing and Machine Translation Key Laboratory of Qinghai Province)

  • Zi-Ke Zhang

    (Alibaba Research Center for Complexity Sciences, Hangzhou Normal University
    Zhejiang University)

Abstract

The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

Suggested Citation

  • Feng Hu & Lin Ma & Xiu-Xiu Zhan & Yinzuo Zhou & Chuang Liu & Haixing Zhao & Zi-Ke Zhang, 2021. "The aging effect in evolving scientific citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4297-4309, May.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03929-8
    DOI: 10.1007/s11192-021-03929-8
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