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Modelling citation networks

Author

Listed:
  • S. R. Goldberg

    (Queen Mary University of London)

  • H. Anthony

    (Imperial College London)

  • T. S. Evans

    (Imperial College London)

Abstract

The distribution of the number of academic publications against citation count for papers published in the same year is remarkably similar from year to year. We characterise the shape of such distributions by a ‘width’, $$\sigma ^2$$ σ 2 , associated with fitting a log-normal to each distribution, and find the width to be approximately constant for publications published in different years. This similarity is not surprising, after all, why would papers in a given year be cited more than another year? Nevertheless, we show that simple citation models fail to capture this behaviour. We then provide a simple three parameter citation network model which can reproduce the correct width over time. We use the citation network of papers from the hep-th section of arXiv to test our model. Our final model reproduces the data’s observed ‘width’ when around 20 % of the citations in the model are made to recently published papers in the entire network (‘global information’). The remaining 80 % of citations are made using the references from these papers’ bibliographies (‘local searches’). We note that this is consistent with other studies, though our motivation to achieve the above distribution with time is very different. Finally, we find that, in the citation network model, varying the number of papers referenced by a new publication is important as it alters the parameters in the model which are fitted to the data. This is not addressed in current models and needs further work.

Suggested Citation

  • S. R. Goldberg & H. Anthony & T. S. Evans, 2015. "Modelling citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1577-1604, December.
  • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1737-9
    DOI: 10.1007/s11192-015-1737-9
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    References listed on IDEAS

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    2. Yanzhu Hu & Huiyang Zhao & Xinbo Ai, 2016. "Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-25, November.
    3. Maria Cristiana Martini & Elvira Pelle & Francesco Poggi & Andrea Sciandra, 2022. "The role of citation networks to explain academic promotions: an empirical analysis of the Italian national scientific qualification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5633-5659, October.
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    6. Jelnov, Pavel & Weiss, Yoram, 2022. "Influence in economics and aging," Labour Economics, Elsevier, vol. 77(C).
    7. Xie, Zheng & Ouyang, Zhenzheng & Liu, Qi & Li, Jianping, 2016. "A geometric graph model for citation networks of exponentially growing scientific papers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 167-175.
    8. Martin Ho & Henry CW Price & Tim S Evans & Eoin O'Sullivan, 2023. "Order in Innovation," Papers 2302.13076, arXiv.org.
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