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Related Experiment Videos

Generation of complex bipartite graphs by using a preferential rewiring process.

Jun Ohkubo1, Kazuyuki Tanaka, Tsuyoshi Horiguchi

  • 1Department of System Information Sciences, Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan. jun@smapip.is.tohoku.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 26, 2005
PubMed
Summary

We developed a model for complex bipartite graphs using preferential rewiring and fitness distributions. This model generates scale-free-like graphs and shows edge condensation under specific conditions.

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Area of Science:

  • Statistical physics
  • Computer science
  • Sociology

Background:

  • Investigating complex bipartite graphs is crucial in computer science and sociology.
  • Understanding these networks requires approaches from statistical physics.

Purpose of the Study:

  • To propose a novel model for generating complex bipartite graphs.
  • To analyze the properties of these graphs, including their stationary state and structural characteristics.

Main Methods:

  • A preferential rewiring process is employed to model edge dynamics.
  • A fitness distribution function is incorporated to influence graph structure.
  • The model generates bipartite graphs with fixed numbers of nodes and edges.

Main Results:

Related Experiment Videos

  • The bipartite graph model reaches a stationary state over time.
  • A scale-free-like property emerges with appropriate fitness distributions.
  • Edge condensation phenomena are observed for certain fitness distributions.

Conclusions:

  • The proposed model effectively generates complex bipartite graphs with tunable properties.
  • The findings suggest that preferential rewiring and fitness distributions are key to achieving scale-free characteristics.
  • The study highlights the potential for edge condensation in such networks.