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Assortative model for social networks.

Michele Catanzaro1, Guido Caldarelli, Luciano Pietronero

  • 1INFM UdR ROMA 1 Dipartimento di Fisica, Università di Roma La Sapienza, Piazzale A. Moro 2, 00185 Roma, Italy.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 5, 2004
PubMed
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We developed a network growth model to understand social networks, specifically analyzing collaborations on the arXiv preprint server. This model explains how scientists connect, revealing insights into the structure of scientific collaboration networks.

Area of Science:

  • Network science
  • Computational social science
  • Bibliometrics

Background:

  • Social networks exhibit complex structures.
  • Understanding the dynamics of scientific collaboration is crucial.
  • Preprint archives like arXiv represent large-scale scientific collaboration networks.

Purpose of the Study:

  • To present a generalized network growth model for social networks.
  • To apply the model to the arXiv preprint archive.
  • To gain insights into the microscopic dynamics of scientific collaboration graphs.

Main Methods:

  • Developed a generalized network growth model.
  • Applied the model to analyze the collaboration network of scientists on arXiv.
  • Characterized the network as degree-assortative.

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Main Results:

  • The model successfully reproduces the behavior of social networks.
  • The arXiv collaboration graph is identified as a degree-assortative network.
  • The model provides insights into the underlying microscopic dynamics.

Conclusions:

  • The generalized network growth model effectively describes social network behavior.
  • Degree-assortativity is a key feature of scientific collaboration networks.
  • The model offers a framework for understanding the formation of scientific collaborations.