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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-03-19
摘要
A plethora of methods have been developed in the past two decades to infer the underlying 網絡 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 非線性 dynamics (SINDy) to reconstruct hypergraphs and simplicial complexes from time-series data. Our model-free method does not require 資訊 about node dynamics or coupling functions, making it applicable to 複雜系統s that do not have a reliable 數學 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 大腦 from resting-state EEG data, which reveals significant contr
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