影响者网络中的相干共振
Coherence resonance in influencer networks
AI总结：
本文研究影响者网络在随机噪声作用下的振荡动力学，发现对影响者施加最优强度噪声可显著增强网络同步，提出“影响者网络中的相干共振”这一新效应，并揭示其源于网络结构与随机性的非线性协同作用。
摘要：
复杂网络在自然界中大量存在，其中许多网络共享一个重要的结构特性：它们包含少数异常高度连接的节点（枢纽）。其中一些枢纽被称为影响者，因为它们与网络强耦合，并在动力学和结构上扮演基础性角色。引人注目的是，尽管带有影响者的网络大量存在，但关于它们对随机强迫的响应却知之甚少。在此，针对影响者网络上的振荡动力学，我们表明，使影响者受到最优强度的噪声作用可以导致网络同步增强。这种新的网络动力学效应，我们称之为影响者网络中的相干共振，源于网络结构与随机性之间的协同作用，并且具有高度非线性，当噪声过弱或过强时该效应会消失。我们的结果揭示，影响者骨干可以急剧增强耦合振子复杂系统中的动力学响应。
英文摘要：
Complex networks are abundant in nature and many share an important structural property: they contain a few nodes that are abnormally highly connected (hubs). Some of these hubs are called influencers because they couple strongly to the network and play fundamental dynamical and structural roles. Strikingly, despite the abundance of networks with influencers, little is known about their response to stochastic forcing. Here, for oscillatory dynamics on influencer networks, we show that subjecting influencers to an optimal intensity of noise can result in enhanced network synchronization. This new network dynamical effect, which we call coherence resonance in influencer networks, emerges from a synergy between network structure and stochasticity and is highly nonlinear, vanishing when the noise is too weak or too strong. Our results reveal that the influencer backbone can sharply increase the dynamical response in complex systems of coupled oscillators.
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