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A geometric graph model for coauthorship networks

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  • Xie, Zheng
  • Ouyang, Zhenzheng
  • Li, Jianping

Abstract

Modeling coauthorship networks helps to understand the emergence and propagation of thoughts in academic society. A random geometric graph is proposed to model coauthorship networks, the connection mechanism of which expresses the effects of the academic influences and homophily of authors, and the collaborations between research teams. Our analysis reveals that the modeled networks have a range of features of empirical coauthorship networks, namely, the degree distribution made up of a mixture Poisson distribution with a power-law tail, clear community structure, small-world, high clustering, and degree assortativity. Moreover, the underlying formulae of the tail and forepart of the degree distribution, and the tail of the scaling relation between local clustering coefficient and degree are derived for the modeled networks, and are also applicable to the empirical networks.

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  • Xie, Zheng & Ouyang, Zhenzheng & Li, Jianping, 2016. "A geometric graph model for coauthorship networks," Journal of Informetrics, Elsevier, vol. 10(1), pages 299-311.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:1:p:299-311
    DOI: 10.1016/j.joi.2016.02.001
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    Cited by:

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    2. 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.
    3. Zheng Xie, 2019. "A cooperative game model for the multimodality of coauthorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 503-519, October.
    4. Xie, Zheng, 2020. "Predicting publication productivity for researchers: A piecewise Poisson model," Journal of Informetrics, Elsevier, vol. 14(3).
    5. Türker, İlker, 2018. "Generating clustered scale-free networks using Poisson based localization of edges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 72-85.
    6. 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.
    7. Tiandong Wang & Sidney Resnick, 2023. "Poisson Edge Growth and Preferential Attachment Networks," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-25, March.
    8. Zhang, Ronda J. & Ye, Fred Y., 2020. "Measuring similarity for clarifying layer difference in multiplex ad hoc duplex information networks," Journal of Informetrics, Elsevier, vol. 14(1).
    9. Wumei Du & Zheng Xie & Yiqin Lv, 2021. "Predicting publication productivity for authors: Shallow or deep architecture?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5855-5879, July.
    10. Xie, Zheng, 2020. "Predicting the number of coauthors for researchers: A learning model," Journal of Informetrics, Elsevier, vol. 14(2).
    11. Orzechowski, Kamil P. & Mrowinski, Maciej J. & Fronczak, Agata & Fronczak, Piotr, 2023. "Asymmetry of social interactions and its role in link predictability: The case of coauthorship networks," Journal of Informetrics, Elsevier, vol. 17(2).
    12. João Génio & Alina Trifan & António J. R. Neves, 2023. "Knowledge Maps as Support Tool for Managing Scientific Competences: A Case Study at a Portuguese Research Institute," Publications, MDPI, vol. 11(1), pages 1-10, March.
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