Noise-induced network topologies

Abstract

We analyze transport on a graph with multiple constraints and where the weight of the edges connecting the nodes is a dynamical variable. The network dynamics results from the interplay between a nonlinear function of the flow, dissipation, and Gaussian, additive noise. For a given set of parameters and finite noise amplitudes, the network self-organizes into one of several metastable configurations, according to a probability distribution that depends on the noise amplitude α. At a finite value α, we find a resonant-like behavior for which one network topology is the most probable stationary state. This specific topology maximizes the robustness and transport efficiency, it is reached with the maximal convergence rate, and it is not found by the noiseless dynamics. We argue that this behavior is a manifestation of noise-induced resonances in network self-organization. Our findings show that stochastic dynamics can boost transport on a nonlinear network and, further, suggest a change of paradigm about the role of noise in optimization algorithms.

8 pages, 7 figures, Version published in PRL

References in corpus (7)

• The Variational Quantum Eigensolver: a review of methods and best practices
• Extracting the multiscale backbone of complex weighted networks
• Noise-Induced Synchronization and Clustering in Ensembles of Uncoupled Limit-Cycle Oscillators
• Optimal non-Markovian search strategies with n-step memory
• Multicommodity routing optimization for engineering networks
• Efficient light harvesting and photon sensing via engineered cooperative effects
• Interplay of periodic dynamics and noise: insights from a simple adaptive system

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• Optimal transport with constraints: from mirror descent to classical mechanics
• Vulnerability of Transport through Evolving Spatial Networks
• Self-organized transport in noisy dynamic networks