Explosive synchronization in temporal networks: A comparative study
arXiv:2007.09801 · doi:10.1063/5.0023329
Abstract
We present a comparative study on Explosive Synchronization (ES) in temporal networks consisting of phase oscillators. The temporal nature of the networks is modeled with two configurations: (1) oscillators are allowed to move in a closed two dimensional box such that they couple with their neighbors, (2) oscillators are static and they randomly switch their coupling partners. Configuration (1) is further studied under two possible scenarios: in the first case oscillators couple to fixed numbers of neighbors while in other they couple to all oscillators lying in their circle of vision. Under these circumstances, we monitor the degrees of temporal networks, velocities, and radius of circle of vision of the oscillators, and the probability of forming connections in order to study and compare the critical values of the coupling required to induce ES in the population of phase oscillators.
References in corpus (16)
- Synchronization in complex networks
- Chimera States for Coupled Oscillators
- The Kuramoto model in complex networks
- Explosive Synchronization Transitions in Scale-free Networks
- Network Synchronization, Diffusion, and the Paradox of Heterogeneity
- Explosive synchronization in adaptive and multilayer networks
- Explosive transitions in complex networks' structure and dynamics: percolation and synchronization
- The onset of synchronization in large networks of coupled oscillators
- Explosive first-order transition to synchrony in networked chaotic oscillators
- Cluster Explosive Synchronization in Complex Networks
- Explosive transitions to synchronization in networked phase oscillators
- Critical synchronization dynamics of the Kuramoto model on connectome and small world graphs
- Tuning synchronization of integrate-and-fire oscillators through mobility
- Synchronization in dynamical networks of locally coupled self-propelled oscillators
- Amplification of explosive width in complex networks
- Predictive modelling of disease propagation in a mobile, connected community