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Network assembly of scientific communities of varying size and specificity

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  • Citron, Daniel T.
  • Way, Samuel F.

Abstract

How does the collaboration network of researchers coalesce around a scientific topic? What sort of social restructuring occurs as a new field develops? Previous empirical explorations of these questions have examined the evolution of co-authorship networks associated with several fields of science, each noting a characteristic shift in network structure as fields develop. Historically, however, such studies have tended to rely on manually annotated datasets and therefore only consider a handful of disciplines, calling into question the universality of the observed structural signature. To overcome this limitation and test the robustness of this phenomenon, we use a comprehensive dataset of over 189,000 scientific articles and develop a framework for partitioning articles and their authors into coherent, semantically related groups representing scientific fields of varying size and specificity. We then use the resulting population of fields to study the structure of evolving co-authorship networks. Consistent with earlier findings, we observe a global topological transition as the co-authorship networks coalesce from a disjointed aggregate into a dense giant connected component that dominates the network. We validate these results using a separate, complimentary corpus of scientific articles, and, overall, we find that the previously reported characteristic structural evolution of a scientific field's associated co-authorship network is robust across a large number of scientific fields of varying size, scope, and specificity. Additionally, the framework developed in this study may be used in other scientometric contexts in order to extend studies to compare across a larger range of scientific disciplines.

Suggested Citation

  • Citron, Daniel T. & Way, Samuel F., 2018. "Network assembly of scientific communities of varying size and specificity," Journal of Informetrics, Elsevier, vol. 12(1), pages 181-190.
  • Handle: RePEc:eee:infome:v:12:y:2018:i:1:p:181-190
    DOI: 10.1016/j.joi.2017.12.008
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    References listed on IDEAS

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    1. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    2. Vincent Larivière & Cassidy R. Sugimoto & Benoit Macaluso & Staša Milojević & Blaise Cronin & Mike Thelwall, 2014. "arXiv E-prints and the journal of record: An analysis of roles and relationships," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(6), pages 1157-1169, June.
    3. Bettencourt, Luís M.A. & Kaiser, David I. & Kaur, Jasleen, 2009. "Scientific discovery and topological transitions in collaboration networks," Journal of Informetrics, Elsevier, vol. 3(3), pages 210-221.
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    2. B. S. A. S. Rajita & Samarth Soni & Deepa Kumari & Subhrakanta Panda, 2024. "An empirical framework for event prediction in massive datasets," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 2880-2901, July.

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