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Modeling the coevolution between citations and coauthorship of scientific papers

Author

Listed:
  • Zheng Xie

    (National University of Defense Technology)

  • Zonglin Xie

    (National University of Defense Technology)

  • Miao Li

    (Shanghai Jiao Tong University)

  • Jianping Li

    (National University of Defense Technology)

  • Dongyun Yi

    (National University of Defense Technology)

Abstract

Collaborations and citations within scientific research grow simultaneously and interact dynamically. Modelling the coevolution between them helps to study many phenomena that can be approached only through combining citation and coauthorship data. A geometric graph for the coevolution is proposed, the mechanism of which synthetically expresses the interactive impacts of authors and papers in a geometrical way. The model is validated against a dataset of papers published on PNAS during 2007–2015. The validation shows the ability to reproduce a range of features observed with citation and coauthorship data combined and separately. Particularly, in the empirical distribution of citations per author there exist two limits, in which the distribution appears as a generalized Poisson and a power-law respectively. Our model successfully reproduces the shape of the distribution, and provides an explanation for how the shape emerges via the decisions of authors. The model also captures the empirically positive correlations between the numbers of authors’ papers, citations and collaborators.

Suggested Citation

  • Zheng Xie & Zonglin Xie & Miao Li & Jianping Li & Dongyun Yi, 2017. "Modeling the coevolution between citations and coauthorship of scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 483-507, July.
    • Handle: RePEc:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2359-1
    DOI: 10.1007/s11192-017-2359-1
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    References listed on IDEAS

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    Citations

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    Cited by:

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    3. Li, Jingjing & Zhang, Jian & Li, Huajiao & Jiang, Meihui, 2018. "Network and community structure in a scientific team with high creative performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 702-709.
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    6. Xie, Zheng, 2020. "Predicting the number of coauthors for researchers: A learning model," Journal of Informetrics, Elsevier, vol. 14(2).

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