2014/04/11 by Ankit Kumar, Kumar, Ankit, Vidit Agrawal +3
Computer Science · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #FOS: Physical sciences #Neural Networks Stability and Synchronization #Nonlinear Dynamics and Pattern Formation #Opinion Dynamics and Social Influence
paper · pdf · doi:10.48550/arxiv.1404.3196
openalex publication_date 2014/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work we investigate time varying networks with complex dynamics at the nodes. We consider two scenarios of network change in an interval of time: first, we have the case where each link can change with probability pt, i.e. the network changes occur locally and independently at each node. Secondly we consider the case where the entire connectivity matrix changes with probability pt, i.e. the change is global. We show that network changes, occurring both locally and globally, yield an enhanced range of synchronization. When the connections are changed slowly (i.e. pt is low) the nodes display nearly synchronized intervals interrupted by intermittent unsynchronized chaotic bursts. However when the connections are switched quickly (i.e. pt is large), the intermittent behavior quickly settles down to a steady synchronized state. Furthermore we find that the mean time taken to reach synchronization from generic random initial states is significantly reduced when the underlying links change more rapidly. We also analyze the probabilistic dynamics of the system with changing connectivity and the stable synchronized range thus obtained is in broad agreement with those observed numerically.