论文标题
噪声引起的网络拓扑
Noise-induced network topologies
论文作者
论文摘要
我们分析具有多个约束的图表上的传输，以及连接节点的边缘的重量是一个动态变量。网络动力学是由流动，耗散和高斯添加噪声的非线性函数之间的相互作用引起的。对于给定的一组参数和有限的噪声幅度，该网络根据取决于噪声振幅α的概率分布，将网络自组织为几个亚稳态构型之一。在有限的值α上，我们发现一种类似谐振的行为，一种网络拓扑是最可能的固定状态。这种特定的拓扑结构可最大程度地提高鲁棒性和运输效率，并以最大收敛速率达到，并且没有噪音的动力学发现。我们认为，这种行为是网络自组织中噪声引起的共振的体现。我们的发现表明，随机动力学可以增强在非线性网络上的传输，进一步提出了关于噪声在优化算法中作用的范式的变化。
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.
