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Tiago Pereira1, Deniz Eroglu2, G Baris Bagci2

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We investigated how to achieve coherence in complex networks. Findings show that network structure and element dynamics precisely control coherence, offering methods for emergent property manipulation.

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Area of Science:

  • Complex Systems
  • Network Science
  • Dynamical Systems

Background:

  • Understanding emergent properties in complex networks is crucial.
  • Coherence is a key emergent property in many natural and artificial systems.
  • The interplay between network structure and individual element dynamics governs system-level behavior.

Purpose of the Study:

  • To precisely determine the factors influencing the emergence of dynamical coherence in complex networks.
  • To establish relationships between network connectivity, element dynamics, coupling functions, and overall coherence.
  • To provide insights into controlling emergent network properties.

Main Methods:

  • Analysis of mutually coupled nonidentical elements within various network topologies.
  • Mathematical modeling to derive the dependence of coherence on network parameters.
  • Simulations on random and locally connected graphs to validate theoretical predictions.

Main Results:

  • Coherence in random graphs scales proportionally with the mean degree.
  • In locally connected networks, coherence depends on how mean degree scales with network size.
  • Introducing a small fraction of random connections significantly enhances coherence in locally connected networks.

Conclusions:

  • Dynamical coherence is precisely controllable through network connectivity and element dynamics.
  • Network topology significantly impacts coherence, with different scaling laws for random versus local connections.
  • Targeted addition of random connections offers a strategy to enhance coherence in otherwise uncoupled systems.