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Collective Phenomena Emerging from the Interactions between Dynamical Processes in Multiplex Networks.

Vincenzo Nicosia1, Per Sebastian Skardal2, Alex Arenas3

  • 1School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom.

Physical Review Letters
|April 15, 2017
PubMed
Summary
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This study introduces a multilayer network framework to couple different dynamical processes. The framework reveals how interacting systems, like neural dynamics and nutrient transport, can achieve explosive synchronization and heterogeneous distributions.

Area of Science:

  • Complex Systems Science
  • Network Science
  • Computational Neuroscience
  • Biophysics

Background:

  • Dynamical processes often occur on distinct network structures.
  • Interactions between different dynamical systems can lead to emergent phenomena.
  • Understanding coupled dynamics is crucial for various scientific fields.

Purpose of the Study:

  • To develop a general framework for intertwining distinct dynamical processes using multilayer networks.
  • To investigate emergent collective phenomena in bidirectionally coupled systems.
  • To analyze the impact of multilayer coupling on synchronization and resource allocation.

Main Methods:

  • Development of a multilayer network framework to model coupled dynamical processes.

Related Experiment Videos

  • Numerical simulations of a model coupling neural dynamics and nutrient transport.
  • Analytical proofs to support numerical findings on system behavior.

    • Main Results:

    • Multilayer coupling induces spontaneous explosive synchronization in the coupled systems.
    • The interaction leads to a heterogeneous distribution of transport allocations.
    • These phenomena are not observed when systems are considered in isolation.

    Conclusions:

    • The proposed multilayer network framework effectively models and reveals emergent behaviors in coupled dynamical systems.
    • Bidirectional coupling can fundamentally alter system dynamics, inducing synchronization and heterogeneity.
    • The framework has broad applicability to diverse interacting dynamical processes like opinion formation and disease spread.