Path-integral approach to the dynamics in sparse random network
arXiv:cond-mat/0507285 · doi:10.1103/PhysRevE.72.016109
Abstract
We study the dynamics involved in a sparse random network model. We extend the standard mean-field approximation for the dynamics of a random network by employing the path-integral approach. The result indicates that the distribution of the variable is essentially identical to that obtained from globally coupled oscillators with random Gaussian interaction. We present the results of a numerical simulation of the Kuramoto transition in a random network, which is found to be consistent with this analysis.
References in corpus (10)
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Epidemic spreading in scale-free networks
- Evolution of networks
- The onset of synchronization in large networks of coupled oscillators
- Synchronization of Kuramoto Oscillators in Scale-Free Networks
- Frequency synchronization in random oscillator network
- Fitness for Synchronization of Network Motifs
- Mean-field dynamics of sequence processing neural networks with finite connectivity
- Neural network learning dynamics in a path integral framework
Cited by in corpus (13)
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- Kuramoto model with frequency-degree correlations on complex networks
- Dynamical Systems on Networks: A Tutorial
- Strong Effects of Network Architecture in the Entrainment of Coupled Oscillator Systems
- The emergence of coherence in complex networks of heterogeneous dynamical systems
- Link-disorder fluctuation effects on synchronization in random networks
- Reduction of oscillator dynamics on complex networks to dynamics on complete graphs through virtual frequencies
- Dynamical Theory for Adaptive Systems
- Synchronization in an evolving network
- Irreversibility in Non-reciprocal Chaotic Systems
- How to grow an oscillator network with enhanced synchronization