论文标题
来自不完整和嘈杂数据的复杂网络动力学的自主推断
Autonomous inference of complex network dynamics from incomplete and noisy data
论文作者
论文摘要
近年来，捕获复杂网络系统的结构和行为的经验数据的可用性已大大增加，但是，一种用于揭示复杂系统的淋巴结和相互作用动态的多功能计算工具箱仍然难以捉摸。在这里，我们开发了一种两阶段的方法来自主推断复杂的网络动力学，其有效性是通过推断神经元，遗传，社会和耦合振荡器在各种合成和真实网络上的动力学的测试来证明的。重要的是，这种方法对不完整和噪声是可靠的，包括低分辨率，观察和动态噪声，缺失和虚假的链接以及动态异质性。我们将两阶段方法应用于全球航空网络上H1N1流感的早期扩散动力学，并且推断的动力学方程还可以捕获SARS和COVID-199疾病的扩散。这些发现共同提供了一条途径，以发现各种各样的真实网络系统的隐藏微观机制。
The availability of empirical data that capture the structure and behavior of complex networked systems has been greatly increased in recent years, however a versatile computational toolbox for unveiling a complex system's nodal and interaction dynamics from data remains elusive. Here we develop a two-phase approach for autonomous inference of complex network dynamics, and its effectiveness is demonstrated by the tests of inferring neuronal, genetic, social, and coupled oscillators dynamics on various synthetic and real networks. Importantly, the approach is robust to incompleteness and noises, including low resolution, observational and dynamical noises, missing and spurious links, and dynamical heterogeneity. We apply the two-phase approach to inferring the early spreading dynamics of H1N1 flu upon the worldwide airline network, and the inferred dynamical equation can also capture the spread of SARS and COVID-19 diseases. These findings together offer an avenue to discover the hidden microscopic mechanisms of a broad array of real networked systems.
