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Highly clustered scale-free networks.

Konstantin Klemm1, Víctor M Eguíluz

  • 1Center for Chaos and Turbulence Studies, Niels Bohr Institute, Blegdamsvej 17, DK-2100 Copenhagen Ø, Denmark. klemm@nbi.dk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 23, 2002
PubMed
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This study introduces a network growth model demonstrating that finite node memory preserves key network features like power-law degree distribution. Memory effects are crucial for accurately modeling growing complex networks.

Area of Science:

  • Complex systems
  • Network science
  • Statistical physics

Background:

  • Real-world networks exhibit scale-free properties and preferential attachment.
  • Understanding the dynamics of growing networks is crucial for various scientific domains.
  • Previous models often overlook the role of historical node information.

Purpose of the Study:

  • To propose a novel model for growing networks incorporating finite node memory.
  • To investigate how memory affects network topology and dynamics.
  • To explain the emergence of scale-free properties and high clustering in growing networks.

Main Methods:

  • Development of a network growth model based on finite node memory.
  • Analysis of network properties including degree distribution and clustering coefficient.

Related Experiment Videos

  • Comparison of model-generated networks with real-world network data.

    • Main Results:

    • The model reproduces stylized features of real-world networks: power-law degree distribution and linear preferential attachment.
    • Degree distribution is conserved even when considering only recent network growth.
    • The model achieves high clustering values comparable to real-world networks.

    Conclusions:

    • Finite node memory is a critical factor in describing the dynamics of growing networks.
    • The proposed model successfully captures essential topological features of complex networks.
    • Memory effects explain the observed high clustering in scale-free networks.