Abstract

Hyperauthorship, a phenomenon whereby there are a disproportionately large number of authors on a single paper, is increasingly common in several scientific disciplines, but with unknown consequences for network metrics used to study scientific collaboration. The validity of coauthorship as a proxy for scientific collaboration is affected by this. Using bibliometric data from publications in the field of genomics, we examine the impact of hyperauthorship on metrics of scientific collaboration, and propose a method to determine a suitable cutoff threshold for hyperauthored papers and compare coauthorship networks with and without hyperauthored works. Our analysis reveals that including hyperauthored papers dramatically impacts the structural positioning of central authors and the topological characteristics of the network, while producing small influences on whole-network cohesion measures. We present two solutions to minimize the impact of hyperauthorship: using a mathematically grounded and reproducible calculation of threshold cutoff to exclude hyperauthored papers or fractional counting to weight network results. Our findings affirm the structural influences of hyperauthored papers and suggest that scholars should be mindful when using coauthorship networks to study scientific collaboration.

Original languageEnglish (US)
Pages (from-to)613-636
Number of pages24
JournalQuantitative Science Studies
Volume5
Issue number3
DOIs
StatePublished - Jun 1 2024

Keywords

  • bibliometrics
  • bipartite projection
  • egocentric network analysis
  • hyperauthorship
  • network weighting functions
  • scientific collaboration

ASJC Scopus subject areas

  • Analysis
  • Numerical Analysis
  • Cultural Studies
  • Library and Information Sciences

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