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Related Experiment Videos

Generalized local-world models for weighted networks.

Zaofeng Pan1, Xiang Li, Xiaofang Wang

  • 1Complex Networks and Control Lab, Department of Automation, Shanghai Jiao Tong University, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|June 29, 2006
PubMed
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We introduce two generalized local-world (GLW) models for weighted complex networks. These models create networks that bridge exponential and scale-free properties, shifting from assortative to disassortative structures.

Area of Science:

  • Complex Networks
  • Network Theory
  • Statistical Physics

Background:

  • Existing weighted scale-free network models, like Barrat et al.'s, provide a foundation.
  • The local-world concept offers insights into network behavior at a micro-level.
  • Understanding the transition between network types is crucial for modeling real-world systems.

Purpose of the Study:

  • To propose two novel generalized local-world (GLW) models for weighted complex networks.
  • To analyze the network properties generated by these GLW models.
  • To investigate the crossover behavior and assortativity changes in the generated networks.

Main Methods:

  • Theoretical analysis of the proposed generalized local-world models.
  • Numerical simulations to validate theoretical predictions.

Related Experiment Videos

  • Examination of network properties including degree distribution and assortativity.

    • Main Results:

    • The GLW models generate weighted networks exhibiting a crossover between exponential and scale-free properties.
    • Simulations confirm the theoretical findings on network structure.
    • A transition from assortative to disassortative network behavior is observed.

    Conclusions:

    • The proposed GLW models offer a new framework for constructing weighted complex networks.
    • These models successfully capture a blend of exponential and scale-free characteristics.
    • The observed shift in assortativity highlights the models' ability to represent dynamic network evolution.