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Modelling transition phenomena of scientific coauthorship networks

Author

Listed:
  • Z. Xie
  • Z. Ouyang
  • J. Li
  • E. Dong
  • D. Yi

Abstract

In a range of scientific coauthorship networks, transitions emerge in degree distribution, in the correlation between degree and local clustering coefficient, etc. The existence of those transitions could be regarded because of the diversity in collaboration behaviors of scientific fields. A growing geometric hypergraph built on a cluster of concentric circles is proposed to model two specific collaboration behaviors, namely the behaviors of research team leaders and those of the other team members. The model successfully predicts the transitions, as well as many common features of coauthorship networks. Particularly, it realizes a process of deriving the complex “scale†free†property from the simple “yes/no†decisions. Moreover, it provides a reasonable explanation for the emergence of transitions with the difference of collaboration behaviors between leaders and other members. The difference emerges in the evolution of research teams, which synthetically addresses several specific factors of generating collaborations, namely the communications between research teams, academic impacts and homophily of authors.

Suggested Citation

  • Z. Xie & Z. Ouyang & J. Li & E. Dong & D. Yi, 2018. "Modelling transition phenomena of scientific coauthorship networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(2), pages 305-317, February.
  • Handle: RePEc:bla:jinfst:v:69:y:2018:i:2:p:305-317
    DOI: 10.1002/asi.23935
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    Citations

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    Cited by:

    1. Zheng Xie, 2021. "A distributed hypergraph model for simulating the evolution of large coauthorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4609-4638, June.
    2. Chen Yang & Tingting Liu & Xiaohong Chen & Yiyang Bian & Yuewen Liu, 2020. "HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 429-449, April.
    3. 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.
    4. Zheng Xie, 2019. "A cooperative game model for the multimodality of coauthorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 503-519, October.
    5. Xie, Zheng, 2020. "Predicting the number of coauthors for researchers: A learning model," Journal of Informetrics, Elsevier, vol. 14(2).
    6. Xie, Zheng, 2020. "Predicting publication productivity for researchers: A piecewise Poisson model," Journal of Informetrics, Elsevier, vol. 14(3).
    7. Jinseok Kim & Jenna Kim, 2020. "Effect of forename string on author name disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(7), pages 839-855, July.
    8. Chao Lu & Yingyi Zhang & Yong‐Yeol Ahn & Ying Ding & Chenwei Zhang & Dandan Ma, 2020. "Co‐contributorship network and division of labor in individual scientific collaborations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1162-1178, October.

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