Identification of Hypergraph Dynamics via Physics-Informed Neural Networks

In many ecological, biological, and social networks, the system dynamics is shaped not only by pairwise couplings but also by higher-order interactions which involve multiple agents simultaneously. However, identifying such higher-order interactions from time-series data remains a significant challenge. In this letter, we propose a novel framework for identifying hypergraph structures using hypergraph physics-informed neural network (HyperPINN). We model the hypergraph dynamics using continuous-time nonlinear differential equations that incorporate both pairwise and higher-order terms. By embedding the governing dynamics into the loss function of neural networks, HyperPINN jointly estimates the state trajectories and infers the unknown hypergraph structure directly from observed time-series data. Numerical experiments on coupled Rössler oscillators and Lotka-Volterra dynamics demonstrate that HyperPINN reliably uncovers hypergraph structures even under noise and limited data, outperforming state-of-the-art methods.

• Publication year

  2025

• Venue

  IEEE Control Systems Letters

• Publication date

  Unknown publication date

• Fields of study

  Physics, Computer Science

• Identifiers
  DOI 10.1109/LCSYS.2025.3631576
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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