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Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
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Random temporal connections promote network synchronization.

Shijie Zhou1,2,3, Yao Guo1,4, Maoxing Liu5

  • 1Centre for Computational Systems Biology, Fudan University, Shanghai 200433, China.

Physical Review. E
|October 24, 2019
PubMed
Summary
This summary is machine-generated.

Random temporal interactions in complex networks unexpectedly enhance synchronization. This study establishes a criterion to explain how connection randomness, strength, and topology influence this phenomenon, observed in physical and biological systems.

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Area of Science:

  • Complex systems
  • Network science
  • Dynamical systems

Background:

  • Collective dynamics in complex networks often rely on interaction structures.
  • Synchronization is a key phenomenon in many physical and biological systems.
  • The role of random temporal interactions in enhancing synchronization was not well understood.

Purpose of the Study:

  • To report and explain a phenomenon where random temporal interactions enhance synchronization in discrete-time complex networks.
  • To establish a verifiable criterion for this synchronization enhancement.
  • To analyze the impact of statistical characteristics of random temporal connections on synchronization.

Main Methods:

  • Utilized standard synchronization analytics.
  • Applied the theory of stochastic processes.
  • Established a rigorous criterion to analyze synchronization phenomena.

Main Results:

  • Demonstrated that random temporal interaction matrices, even with zero or small average, significantly enhance synchronization with probability one.
  • Rigorously depicted how synchronization is affected by connection strength, topology, and probability distributions.
  • Illustrated the phenomenon in physical and biological complex dynamical networks.

Conclusions:

  • Randomness in temporal interactions is a crucial factor for enhancing synchronization in complex networks.
  • The established criterion provides a quantitative understanding of this phenomenon.
  • The findings have implications for understanding collective behaviors in diverse real-world systems.