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A study on the author collaboration network in big data

Author

Listed:
  • Yufang Peng
  • Jin Shi

    (Nanjing University)

  • Marcelo Fantinato

    (University of São Paulo)

  • Jing Chen

    (Nanjing University)

Abstract

In order to obtain a deeper understanding of the collaboration status in the big data field, we investigated the author collaboration groups and the core author collaboration groups as well as the collaboration trends in big data by combining bibliometric analysis and social network analysis. A total of 4130 papers from 13,759 authors during the period of 2011–2015 was collected. The main results indicate that 3483 of the papers are coauthored (i.e., 84.33% of all papers) from 12,016 coauthors (i.e., 87.33% of all authors), which represent a reputable level of collaboration. On the other hand, 91.83% of all the identified coauthors have published only one paper so far, reflecting a poor level of maturity of such authors. Through social network analysis, we observed that the author collaboration network is composed of small author collaboration groups and also that the authors are mainly from the computer science & technology field. As an important contribution of our study, we further analyzed the author collaboration network, culminating in the generalization of four subnet modes, which were defined by some papers: ‘dual-core’, ‘complete’, ‘bridge’ and ‘sustainable development’. It was found that the dual-core mode stands for the stage that researchers have just begun to study big data. Beginning of big data research, the complete mode tends to joint research, both the dual-core and complete modes are mostly engaged in the same project, and the bridge mode and the sustainable development mode represent, respectively, the popular and valued directions in the big data field. The results of this study can be useful for researchers interested in finding suitable partners in the big data field. By tracking the core authors and the key author collaboration groups, one can learn about the current developments in the big data field as well as predict the development prospects of such a field. Therefore, we expect with the results of our study summarized in this paper to contribute to a faster development of the big data field.

Suggested Citation

  • Yufang Peng & Jin Shi & Marcelo Fantinato & Jing Chen, 2017. "A study on the author collaboration network in big data," Information Systems Frontiers, Springer, vol. 19(6), pages 1329-1342, December.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:6:d:10.1007_s10796-017-9771-1
    DOI: 10.1007/s10796-017-9771-1
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    References listed on IDEAS

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    1. Fuyuki Yoshikane & Takayuki Nozawa & Keita Tsuji, 2006. "Comparative analysis of co-authorship networks considering authors' roles in collaboration: Differences between the theoretical and application areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 643-655, September.
    2. Hildrun Kretschmer & Isidro F. Aguillo, 2004. "Visibility of collaboration on the Web," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(3), pages 405-426, November.
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    Cited by:

    1. Shih-Chia Huang & Suzanne McIntosh & Stanislav Sobolevsky & Patrick C. K. Hung, 2017. "Big Data Analytics and Business Intelligence in Industry," Information Systems Frontiers, Springer, vol. 19(6), pages 1229-1232, December.
    2. Tuba Bircan & Almila Alkim Akdag Salah, 2022. "A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    3. Eunhye Park & Woo-Hyuk Kim, 2022. "A Retrospective Literature Review of Eating Disorder Research (1990–2021): Application of Bibliometrics and Topical Trends," IJERPH, MDPI, vol. 19(13), pages 1-23, June.
    4. Venera Tomaselli & Giovanni Giuffrida & Simona Gozzo & Francesco Mazzeo Rinaldi, 2020. "Building Decision-making Indicators Through Network Analysis of Big Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(1), pages 33-49, August.
    5. Fernando Garrigós-Simón & Silvia Sanz-Blas & Yeamduan Narangajavana & Daniela Buzova, 2021. "The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    6. Xiaozan Lyu & Rodrigo Costas, 2021. "Studying the characteristics of scientific communities using individual-level bibliometrics: the case of Big Data research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6965-6987, August.

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