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A generative model of article citation networks of a subject from a large-scale citation database

Author

Listed:
  • Livia Lin-Hsuan Chang

    (SOKENDAI (The Graduate University for Advanced Studies))

  • Frederick Kin Hing Phoa

    (Institute of Statistical Science, Academia Sinica)

  • Junji Nakano

    (Chuo University)

Abstract

In this paper, we analyze the structure of the article citation network of a particular subject obtained from the Web of Science (WoS) database. In specific, we modify a model proposed in Caldarelli et al. (Phys Rev Lett 89(25):258702, 2002) and develop a generative model for article citation networks in which an article receives citations based on a newly defined property called “importance” introduced in this paper. Since the importance of an article is quantitatively unmeasurable, we consider to use the in-degree of articles, which is the number of citations that an article of interest is cited by other articles, as a surrogate quantity to describe an article’s importance. We simulate some in-degree distributions to estimate the parameters of the tapered Pareto distribution. The generative model shows good performance in the comparison between the generated data and data from the real network, especially the citation network of recent years.

Suggested Citation

  • Livia Lin-Hsuan Chang & Frederick Kin Hing Phoa & Junji Nakano, 2021. "A generative model of article citation networks of a subject from a large-scale citation database," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7373-7395, September.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:9:d:10.1007_s11192-021-04037-3
    DOI: 10.1007/s11192-021-04037-3
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    References listed on IDEAS

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    1. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    2. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
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    Cited by:

    1. Jung, Hohyun, 2023. "Eliminating the biases of user influence and item popularity in bipartite networks: A case study of Flickr and Netflix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

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