Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Synchronization in dynamical networks: evolution along commutative graphs.

S Boccaletti1, D-U Hwang, M Chavez

  • 1CNR-Istituto dei Sistemi Complessi, Largo E. Fermi 6, 50125 Florence, Italy.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 16, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical outcomes of trifluridine/tipiracil plus bevacizumab versus trifluridine/tipiracil or regorafenib in metastatic colorectal cancer: a multicenter cohort study.

ESMO open·2026
Same author

Explainable AI for analyzing the decision of GNNs at predicting dynamic stability of complex oscillator networks.

Chaos (Woodbury, N.Y.)·2025
Same author

Narrative review of pediatric thyroiditis: Diagnosis and management.

American journal of otolaryngology·2025
Same author

Canard Cascading in Networks with Adaptive Mean-Field Coupling.

Physical review letters·2024
Same author

Soluble PD-L1 shows no association to relapse and overall survival in early stage non-small cell lung cancer (NSCLC).

Lung cancer (Amsterdam, Netherlands)·2024
Same author

Detecting local perturbations of networks in a latent hyperbolic embedding space.

Chaos (Woodbury, N.Y.)·2024
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Network synchronizability improves by evolving graphs with time-dependent connectivity matrices. Commuting matrices allow synchronization even when individual graphs do not synchronize, enhancing network dynamics.

Area of Science:

  • Complex systems
  • Network science
  • Dynamical systems

Background:

  • Network synchronizability is crucial for many natural and engineered systems.
  • Achieving synchronization often depends on network structure.
  • Dynamical networks present unique challenges for synchronization.

Purpose of the Study:

  • To investigate methods for improving network synchronizability.
  • To explore the role of time-dependent connectivity matrices in network synchronization.
  • To demonstrate synchronization in dynamical networks using commuting matrices.

Main Methods:

  • Evolving network connectivity using time-dependent matrices.
  • Analyzing networks with commuting connectivity matrices.
  • Comparing different approaches for engineering commutative graphs.

Related Experiment Videos

Main Results:

  • Significant improvement in network synchronizability was achieved.
  • Synchronization was demonstrated in dynamical networks.
  • Synchronization was possible even when individual static graphs within the commuting set did not synchronize.

Conclusions:

  • Time-dependent connectivity matrices offer a powerful tool for enhancing network synchronizability.
  • The framework of commuting matrices provides a viable pathway for achieving synchronization in complex dynamical networks.
  • This approach advances the understanding and control of synchronization phenomena in evolving systems.