Hypergraph reconstruction from dynamics
arxiv(2024)
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 reliable
mathematical descriptions. We first benchmark the new method on synthetic data
generated from Kuramoto and Lorenz dynamics. We then use it to infer the
effective connectivity among seven brain regions from resting-state EEG data,
which reveals significant contributions from non-pairwise interactions in
shaping the macroscopic brain dynamics.
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