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Related Experiment Video

Updated: Jul 4, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Transition to global synchronization in clustered networks.

Shuguang Guan¹, Xingang Wang, Ying-Cheng Lai

¹Temasek Laboratories, National University of Singapore, Singapore 117508.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|June 4, 2008

Summary

Researchers derived a formula for global synchronization in clustered networks, showing how connection probabilities influence synchronization. This work supports findings on how intercluster links enhance network synchronizability.

Related Experiment Videos

Last Updated: Jul 4, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Area of Science:

Complex networks
Network synchronization
Dynamical systems

Background:

Clustered networks feature dense subnetworks with sparse interconnections.
These networks model biological, social, and technological systems.
Global synchronization across clusters is a key research interest.

Purpose of the Study:

To derive an analytic formula for global synchronization in clustered networks.
To investigate the relationship between connection probabilities and synchronization.
To provide numerical verification for the derived formula.

Main Methods:

Utilized Kuramoto-type dynamics for network modeling.
Derived an analytic formula for critical coupling strength.
Performed numerical simulations for verification.

Main Results:

An analytic formula was obtained, linking critical coupling strength to intracluster and intercluster connection probabilities.
Numerical verification confirmed the theoretical results.
Direct support was provided for spectral-analysis findings on intercluster links.

Conclusions:

The derived formula accurately predicts global synchronization in clustered networks.
Intracluster and intercluster connection probabilities are crucial for network synchronizability.
Random intercluster links play a significant role in enhancing synchronization.