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Aggregation process on complex networks.

Luis G Morelli1, Hilda A Cerdeira

  • 1Abdus Salam International Center for Theoretical Physics, P O Box 586, 34100 Trieste, Italy. morelli@ictp.trieste.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 13, 2004
PubMed
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Particle aggregation dynamics on complex networks reveal density decay patterns. Network disorder influences crossover times, impacting particle movement and aggregation behavior on Watts-Strogatz networks.

Area of Science:

  • Complex systems
  • Statistical physics
  • Network science

Background:

  • Particle aggregation is a fundamental process in various scientific fields.
  • Understanding particle dynamics on networks is crucial for modeling complex systems.
  • The Watts-Strogatz model provides a framework for generating networks with tunable disorder.

Purpose of the Study:

  • To investigate particle aggregation dynamics and mass distribution evolution on Watts-Strogatz networks.
  • To analyze the impact of network disorder on particle density decay rates.
  • To compare quenched disorder dynamics with an annealed model.

Main Methods:

  • Simulating random walks of particles on Watts-Strogatz networks.
  • Analyzing particle density decay as a function of time and network disorder.

Related Experiment Videos

  • Developing and studying an annealed model with stochastic long-range jumps.

    • Main Results:

    • Particle density decays as t(-1) on disordered networks and t(-1/2) on regular networks.
    • Intermediate disorder leads to regular network dynamics, while low density reveals disorder effects.
    • Crossover time scales with network disorder (p) as t ~ p(-2) for quenched disorder.
    • The annealed model exhibits a different scaling, with crossover time t ~ p(-1).

    Conclusions:

    • Network topology significantly alters particle aggregation dynamics.
    • The degree of disorder dictates the transition between different decay regimes.
    • The annealed model offers an alternative perspective on disorder effects in aggregation processes.