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Weighted competition scale-free network.

Shijun Wang1, Changshui Zhang

  • 1State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, China. wsj02@mails.tsinghua.edu.cn

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 9, 2005
PubMed
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We introduce a new weighted scale-free network model where node connectivity depends on both its degree and fitness. This model

Area of Science:

  • Complex Systems Science
  • Network Science
  • Statistical Physics

Background:

  • Existing scale-free (SF) network models often use binary random graphs.
  • Node connectivity in many SF networks is primarily determined by arrival time.
  • A need exists for more sophisticated SF network models that account for multiple node attributes.

Purpose of the Study:

  • To propose a novel model for weighted scale-free networks.
  • To incorporate a 'fit-gets-richer' mechanism into network evolution.
  • To analyze the emergent properties of network topology and link weights over time.

Main Methods:

  • Development of a weighted scale-free network model with a 'fit-gets-richer' scheme.
  • Employing a combined numerical and analytical approach for network analysis.

Related Experiment Videos

  • Investigating the evolution of node connectivity and link weights.

    • Main Results:

    • The proposed model demonstrates that node connectivity is influenced by both degree and intrinsic fitness.
    • Asymptotic analysis reveals identical scaling behaviors for total weight distribution and connectivity distribution.
    • The model's findings on scaling behavior are consistent with observations in real-world networks.

    Conclusions:

    • The 'fit-gets-richer' scheme in weighted scale-free networks leads to emergent, identical scaling properties.
    • This model provides a more realistic representation of complex systems compared to binary SF networks.
    • The observed asymptotic sameness in distributions offers insights into real-world network formation and dynamics.