标题： 超图从动力学重建 显示英文标题

标题： Hypergraph reconstruction from dynamics

摘要： 近二十年来，已开发出大量方法，从系统的集体动力学推断其潜在的网络结构。然而，能够推断非成对相互作用的方法才刚刚开始出现。在这里，我们基于非线性动力学的稀疏识别（SINDy）开发了一种推理算法，以从时间序列数据重建超图和单纯复形。我们的无模型方法不需要关于节点动力学或耦合函数的信息，使其适用于那些没有可靠数学描述的复杂系统。我们首先在由Kuramoto和Lorenz动力学生成的合成数据上验证了该新方法。然后，我们使用它从静息态脑电图数据推断大脑中的有效连接，这揭示了非成对相互作用在塑造宏观脑动力学方面的重要贡献。 显示英文摘要

摘要： A plethora of methods have been developed in the past two decades to infer the underlying network structure of an interconnected system from its collective dynamics. However, methods capable of inferring nonpairwise interactions are only starting to appear. Here, we develop an inference algorithm based on sparse identification of nonlinear dynamics (SINDy) to reconstruct hypergraphs and simplicial complexes from time-series data. Our model-free method does not require information about node dynamics or coupling functions, making it applicable to complex systems that do not have a reliable mathematical description. We first benchmark the new method on synthetic data generated from Kuramoto and Lorenz dynamics. We then use it to infer the effective connectivity in the brain from resting-state EEG data, which reveals significant contributions from non-pairwise interactions in shaping the macroscopic brain dynamics.