Patterns of synchronized clusters in adaptive networks

This paper proposes a framework to study patterns of synchronous solutions in adaptive networks with time-varying connectivity. The authors derive a general approach to analyze the stability of these solutions, decoupling the network topology from the dynamics to reduce the dimensionality of the stability problem. The method is applied to three case studies: a network of neural mass models, a two-layer network of coupled phase oscillators, and a random network of chaotic oscillators. The results demonstrate the emergence of multi-stability and the ability to accurately analyze the stability of cluster synchronization solutions.

Matteo Lodi, Shirin Panahi, Francesco Sorrentino, Alessandro Torcini, Marco Storace