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Predicting the number of coauthors for researchers: A learning model

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  • Xie, Zheng

Abstract

Predicting the number of coauthors for researchers contributes to understanding the development of team science. However, it is an elusive task due to diversity in the collaboration patterns of researchers. This study provides a learning model for the dynamics of this variable; the parameters are learned from empirical data that consist of the number of publications and the number of coauthors at given time intervals. The model is based on relationship between the annual number of new coauthors and time given an annual number of publications, the relationship between the annual number of publications and time given a historical number of publications, and Lotka's law. The assumptions of the model are validated by applying it on the high-quality dblp dataset. The effectiveness of the model is tested on the dataset by satisfactory fittings on the evolutionary trend of the number of coauthors for researchers, the distribution of this variable, and the occurrence probability of collaboration events. Due to its regression nature, the model has the potential to be extended to assess the confidence level of the prediction results and thus has applicability to other empirical research.

Suggested Citation

  • Xie, Zheng, 2020. "Predicting the number of coauthors for researchers: A learning model," Journal of Informetrics, Elsevier, vol. 14(2).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:2:s1751157719303852
    DOI: 10.1016/j.joi.2020.101036
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    1. Isabel Gómez & María Teresa Fernández & Jesús Sebastián, 1999. "Analysis of the structure of international scientific cooperation networks through bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 44(3), pages 441-457, March.
    2. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    3. Perc, Matjaž, 2010. "Growth and structure of Slovenia’s scientific collaboration network," Journal of Informetrics, Elsevier, vol. 4(4), pages 475-482.
    4. W. Glänzel & A. Schubert & H. -J. Czerwon, 1999. "A bibliometric analysis of international scientific cooperation of the European Union (1985–1995)," Scientometrics, Springer;Akadémiai Kiadó, vol. 45(2), pages 185-202, June.
    5. Hoekman, Jarno & Frenken, Koen & Tijssen, Robert J.W., 2010. "Research collaboration at a distance: Changing spatial patterns of scientific collaboration within Europe," Research Policy, Elsevier, vol. 39(5), pages 662-673, June.
    6. 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.
    7. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    8. Li, Feng & Miao, Yajun & Yang, Chenchen, 2015. "How do alumni faculty behave in research collaboration? An analysis of Chang Jiang Scholars in China," Research Policy, Elsevier, vol. 44(2), pages 438-450.
    9. van Rijnsoever, Frank J. & Hessels, Laurens K., 2011. "Factors associated with disciplinary and interdisciplinary research collaboration," Research Policy, Elsevier, vol. 40(3), pages 463-472, April.
    10. Zheng Xie, 2019. "A cooperative game model for the multimodality of coauthorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 503-519, October.
    11. Chenwei Zhang & Yi Bu & Ying Ding & Jian Xu, 2018. "Understanding scientific collaboration: Homophily, transitivity, and preferential attachment," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(1), pages 72-86, January.
    12. Wagner, Caroline S. & Leydesdorff, Loet, 2005. "Network structure, self-organization, and the growth of international collaboration in science," Research Policy, Elsevier, vol. 34(10), pages 1608-1618, December.
    13. Melin, Goran, 2000. "Pragmatism and self-organization: Research collaboration on the individual level," Research Policy, Elsevier, vol. 29(1), pages 31-40, January.
    14. Mengjiao Qi & An Zeng & Menghui Li & Ying Fan & Zengru Di, 2017. "Standing on the shoulders of giants: the effect of outstanding scientists on young collaborators’ careers," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1839-1850, June.
    15. 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.
    16. Xie, Zheng & Ouyang, Zhenzheng & Li, Jianping, 2016. "A geometric graph model for coauthorship networks," Journal of Informetrics, Elsevier, vol. 10(1), pages 299-311.
    17. Zheng Xie & Jianping Li & Miao Li, 2018. "Exploring Cooperative Game Mechanisms of Scientific Coauthorship Networks," Complexity, Hindawi, vol. 2018, pages 1-11, July.
    18. Corrêa Jr., Edilson A. & Silva, Filipi N. & da F. Costa, Luciano & Amancio, Diego R., 2017. "Patterns of authors contribution in scientific manuscripts," Journal of Informetrics, Elsevier, vol. 11(2), pages 498-510.
    19. Lorenzo Ductor, 2015. "Does Co-authorship Lead to Higher Academic Productivity?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 385-407, June.
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    Cited by:

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    4. Zheng Xie & Yanwu Li & Zhemin Li, 2020. "Assessing and predicting the quality of research master’s theses: an application of scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 953-972, August.
    5. Xie, Zheng & Lv, Yiqin & Song, Yiping & Wang, Qi, 2024. "Data labeling through the centralities of co-reference networks improves the classification accuracy of scientific papers," Journal of Informetrics, Elsevier, vol. 18(2).
    6. Xie, Qing & Zhang, Xinyuan & Kim, Giyeong & Song, Min, 2022. "Exploring the influence of coauthorship with top scientists on researchers’ affiliation, research topic, productivity, and impact," Journal of Informetrics, Elsevier, vol. 16(3).
    7. 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).

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