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Related Experiment Videos

Group-based Yule model for bipartite author-paper networks.

Michel L Goldstein1, Steven A Morris, Gary G Yen

  • 1Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, USA. michel.goldstein@okstate.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 24, 2005
PubMed
Summary

This study models author-paper networks using group dynamics and a success-breeds-success approach. Simulations show the model accurately reflects real-world collaboration patterns in complex networks research.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Computational Social Science

Background:

  • Understanding the structure and evolution of scientific collaboration is crucial.
  • Author-paper networks offer insights into research dynamics and community formation.
  • Existing models may not fully capture the emergent properties of collaborative research.

Purpose of the Study:

  • To propose a novel model for author-paper networks.
  • To incorporate group dynamics and a success-breeds-success mechanism into network modeling.
  • To simulate and validate the model against real-world data.

Main Methods:

  • Developed a generative model for author-paper networks based on group organization.
  • Implemented a success-breeds-success model for paper publication within research groups.

Related Experiment Videos

  • Modeled inter-group collaboration through random external invitations.
  • Extracted and analyzed network metrics from simulated and real-world datasets.

    • Main Results:

    • The proposed model successfully mimics key characteristics of real-world author-paper networks.
    • Simulations demonstrated the model's ability to replicate emergent collaboration patterns.
    • The success-breeds-success component effectively drives publication output within groups.

    Conclusions:

    • The presented model provides a robust framework for understanding author-paper network formation.
    • Group dynamics and preferential attachment are key drivers in scientific collaboration.
    • The model serves as a valuable tool for analyzing and predicting research network evolution.