University of California at Riverside, Department of Mechanical Engineering, Riverside, 92521, CA, USA.
Westlake University, Shcool of Engineering, Hangzhou, 310024, China.
Sci Rep. 2020 Feb 4;10(1):1774. doi: 10.1038/s41598-020-58440-6.
While numerous studies have suggested that large natural, biological, social, and technological networks are fragile, convincing theories are still lacking to explain why natural evolution and human design have failed to optimize networks and avoid fragility. In this paper we provide analytical and numerical evidence that a tradeoff exists in networks with linear dynamics, according to which general measures of robustness and performance are in fact competitive features that cannot be simultaneously optimized. Our findings show that large networks can either be robust to variations of their weights and parameters, or efficient in responding to external stimuli, processing noise, or transmitting information across long distances. As illustrated in our numerical studies, this performance tradeoff seems agnostic to the specific application domain, and in fact it applies to simplified models of ecological, neuronal, and traffic networks.
虽然许多研究表明,大型自然、生物、社会和技术网络是脆弱的,但仍缺乏令人信服的理论来解释为什么自然进化和人类设计未能优化网络并避免脆弱性。在本文中,我们提供了具有线性动力学的网络中存在权衡的分析和数值证据,根据该权衡,一般的鲁棒性和性能度量实际上是竞争特征,不能同时优化。我们的研究结果表明,大型网络要么可以对其权重和参数的变化具有鲁棒性,要么可以有效地响应外部刺激、处理噪声或在长距离上传输信息。正如我们的数值研究所示,这种性能权衡似乎与特定的应用领域无关,事实上,它适用于生态、神经元和交通网络的简化模型。