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      Coauthorship networks and patterns of scientific collaboration.

      Proceedings of the National Academy of Sciences of the United States of America

      Periodicals as Topic, Neural Networks (Computer), Models, Statistical, Authorship

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          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.

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          Author and article information

          Journal
          10.1073/pnas.0307545100
          387296
          14745042

          Chemistry

          Periodicals as Topic, Neural Networks (Computer), Models, Statistical, Authorship

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