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Updated: Apr 24, 2026

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Synchronization in time-varying networks.

Vivek Kohar1, Peng Ji2, Anshul Choudhary3

  • 1Potsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany and Indian Institute of Science Education and Research (IISER) Mohali, Knowledge City, SAS Nagar, Sector 81, Manauli PO 140 306, Punjab, India.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 13, 2014
PubMed
Summary
This summary is machine-generated.

Dynamic complex networks enhance synchronization stability. Stochastic rewiring improves stability ranges and speeds up synchronization, outperforming static networks, especially at high rewiring frequencies.

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Area of Science:

  • Complex networks
  • Network dynamics
  • Synchronization theory

Background:

  • Basin stability offers a robust, nonlinear measure for assessing the stability of synchronized states in complex systems.
  • Understanding network stability is crucial for applications ranging from power grids to biological systems.

Purpose of the Study:

  • To investigate the impact of time-varying network structures on the stability of synchronized states.
  • To compare the stability of synchronized states in dynamic small-world and random networks.

Main Methods:

  • Utilizing basin stability, a nonlocal and nonlinear measure, to analyze high-dimensional systems.
  • Introducing time-varying network characteristics by stochastically rewiring links at an average frequency (f).

Main Results:

  • Dynamic networks significantly increase the stability range and reduce the time to reach synchronization compared to static networks.
  • Small-world networks exhibit higher sensitivity to link changes than random networks, with dynamic effects appearing at lower rewiring frequencies.
  • At high rewiring frequencies, random networks demonstrate superior performance, maintaining synchronized state stability across a broader range of coupling strengths.

Conclusions:

  • Time-varying network topology can enhance synchronization stability and speed.
  • Network architecture (small-world vs. random) influences sensitivity to dynamic changes, with random networks showing better stability at high rewiring rates.
  • Basin stability and linear stability analysis provide complementary insights into system stability under varying perturbation scales.