The aging effect in evolving scientific citation networks

Feng Hu, Lin Ma, Xiu-Xiu Zhan (), Yinzuo Zhou (), Chuang Liu, Haixing Zhao and Zi-Ke Zhang ()
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Feng Hu: Qinghai Normal University
Lin Ma: Alibaba Research Center for Complexity Sciences, Hangzhou Normal University
Xiu-Xiu Zhan: Alibaba Research Center for Complexity Sciences, Hangzhou Normal University
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
Zi-Ke Zhang: Alibaba Research Center for Complexity Sciences, Hangzhou Normal University

Scientometrics, 2021, vol. 126, issue 5, No 27, 4297-4309

Abstract: 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.

Keywords: Aging effect; Evolution; Hypergraph theory; Scientific citation network (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11192-021-03929-8

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