Clustering and community detection in networks are of broad interest and have been the subject of extensive research that spans several fields. We are interested in the relatively narrow question of detecting communities of scientific publications that are linked by citations. These publication communities can be used to identify scientists with shared interests who form communities of researchers. Building on the well-known k-core algorithm, we have developed a modular pipeline to find publication communities with center–periphery structure. Using a quantitative and qualitative approach, we evaluate community finding results on a citation network consisting of over 14 million publications relevant to the field of extracellular vesicles. We compare our approach to communities discovered by the widely used Leiden algorithm for community finding.

Original languageEnglish (US)
Pages (from-to)289-314
Number of pages26
JournalQuantitative Science Studies
Issue number1
StatePublished - Apr 12 2022


  • bibliometrics
  • clustering
  • community finding
  • exosome
  • extracellular vesicles

ASJC Scopus subject areas

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


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