Coauthorship networks and patterns of scientific collaboration

University of Michigan

PubMed
Indexed incrossrefpubmed

Abstract

By using data from three bibliographic databases in biology, physics, and mathematics, respectively, networks are constructed in which the nodes are scientists, and two scientists are connected if they have coauthored a paper. We use these networks to answer a broad variety of questions about collaboration patterns, such as the numbers of papers authors write, how many people they write them with, what the typical distance between scientists is through the network, and how patterns of collaboration vary between subjects and over time. We also summarize a number of recent results by other authors on coauthorship patterns.

Citation impact

1,933
total citations
FWCI
33.64
Percentile
100%
References
38
Citations per year

Authors

1

Topics & keywords

Keywords
  • Variety (cybernetics)
  • Data science
  • Computer science
  • Artificial intelligence
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Funding