Scientific Co‐Authorship Networks

Cugmas, Marjan; Ferligoj, Anuška; Kronegger, Luka

doi:10.1002/9781119483298.ch13

Cited by 5 publications

(2 citation statements)

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“…Such empirical results have stipulated a science of team science (Börner et al, 2010) studying the dynamics of scientific collaboration and the output (e.g., number of published papers) and impact (e.g., number of citations gathered by published papers) of scientific teams (Guimera et al, 2005;Ahmadpoor & Jones, 2019;Hayat et al, 2020). Coauthor networks provide observable data on large-scale scientific collaboration and are frequently analyzed to uncover collaboration structure and scientific output and impact (Kronegger et al, 2012;Ferligoj et al, 2015;Kumar, 2015;Hâncean & Perc, 2016;Cugmas et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks

Lerner

Hâncean

2022

Net Sci

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We discuss a recently proposed family of statistical network models—relational hyperevent models (RHEMs)—for analyzing team selection and team performance in scientific coauthor networks. The underlying rationale for using RHEM in studies of coauthor networks is that scientific collaboration is intrinsically polyadic, that is, it typically involves teams of any size. Consequently, RHEM specify publication rates associated with hyperedges representing groups of scientists of any size. Going beyond previous work on RHEM for meeting data, we adapt this model family to settings in which relational hyperevents have a dedicated outcome, such as a scientific paper with a measurable impact (e.g., the received number of citations). Relational outcome can on the one hand be used to specify additional explanatory variables in RHEM since the probability of coauthoring may be influenced, for instance, by prior (shared) success of scientists. On the other hand, relational outcome can also serve as a response variable in models seeking to explain the performance of scientific teams. To tackle the latter, we propose relational hyperevent outcome models that are closely related with RHEM to the point that both model families can specify the likelihood of scientific collaboration—and the expected performance, respectively—with the same set of explanatory variables allowing to assess, for instance, whether variables leading to increased collaboration also tend to increase scientific impact. For illustration, we apply RHEM to empirical coauthor networks comprising more than 350,000 published papers by scientists working in three scientific disciplines. Our models explain scientific collaboration and impact by, among others, individual activity (preferential attachment), shared activity (familiarity), triadic closure, prior individual and shared success, and prior success disparity among the members of hyperedges.

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Section: Introductionmentioning

confidence: 99%

Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks

Lerner

Hâncean

2022

Net Sci

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“…Co-authorship has been identified as one of the most tangible and well-documented forms of collaboration among scholars (Cugmas et al , 2017; Kumar, 2015; Melin and Persson, 1996) because it generally requires a working interpersonal relationship. Co-authorship networks provide a lens through which collaborative networks can be extensively analyzed and assessed for trends or changes over time.…”

Section: Introductionmentioning

confidence: 99%

Hidden patterns

Collins

Steffen-Fluhr²

2019

EDI

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Purpose The purpose of this paper is to describe how one group of ADVANCE Project researchers investigated faculty co-authorship networks to identify relationships between women’s positions in these networks, their research productivity and their advancement at the university – and to make those relationships transparent. Design/methodology/approach Multiple methods for capturing faculty network data were evaluated, including collecting self-reported data and mining bibliometric data from various web-based sources. Faculty co-authorship networks were subsequently analyzed using several methodologies including social network analysis (SNA), network visualizations and the Kaplan–Meier product limit estimator. Findings Results suggest that co-authorship provides an important way for faculty to signal the value of their work, meaning that co-authoring with many others may be beneficial to productivity and promotion. However, patterns of homophily indicate that male faculty tend to collaborate more with other men, reducing signaling opportunities for women. Visualizing these networks can assist faculty in finding and connecting with new collaborators and can provide administrators with unique views of the interactions within their organizations. Finally, Kaplan–Meier survival studies showed longitudinal differences in the retention and advancement of faculty based on gender. Originality/value Together, these findings begin to shed light on subtle differences that, over time, may account for the significant gender disparities at STEM institutions, patterns which should be investigated and addressed by administrators. Lessons learned, as well as the novel use of SNA and Kaplan–Meier in investigating gender differences in STEM faculty, provide important findings for other researchers seeking to conduct similar studies at their own institutions.

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Use of Benford's law on academic publishing networks

Tošić

Vičič

2021

Journal of Informetrics

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Scientific Co‐Authorship Networks

Cited by 5 publications

References 29 publications

Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks

Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks

Hidden patterns

Use of Benford's law on academic publishing networks

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