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Related Experiment Videos

Generating structured networks based on a weight-dependent deactivation mechanism.

Zhi-Xi Wu1, Xin-Jian Xu, Ying-Hai Wang

  • 1Institute of Theoretical Physics, Lanzhou University, Lanzhou Gansu 730000, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 11, 2005
PubMed
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This study introduces a weight-dependent model for evolving networks, generating structures with high clustering coefficients. The model produces weighted, scale-free or exponential networks based on vertex selection methods.

Area of Science:

  • Network Science
  • Statistical Physics
  • Complex Systems

Background:

  • Existing models like the degree-dependent deactivation model generate networks with high clustering coefficients.
  • Evolving networks require models that account for both vertex activity and weight dynamics.

Purpose of the Study:

  • To introduce and analyze a weight-dependent deactivation mechanism for modeling evolving networks.
  • To investigate the network properties, specifically clustering coefficient and structure, generated by this new mechanism.

Main Methods:

  • Developed a naive weight-driven deactivation mechanism.
  • Employed analytical solutions and numerical simulations to study network growth dynamics.
  • Investigated two vertex selection strategies: target selection and random selection.

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Main Results:

  • Generated networks exhibit a high clustering coefficient, surpassing that of regular lattices with similar average connectivity.
  • Target selection of deactivated vertices yields weighted, structured scale-free networks.
  • Random selection of deactivated vertices results in weighted, structured exponential networks.

Conclusions:

  • The weight-dependent deactivation model effectively generates evolving networks with significant clustering.
  • The choice of vertex selection strategy dictates the emergent network topology (scale-free or exponential).
  • This model provides a framework for creating weighted, structured complex networks.