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HGNNv2: Stable Hypergraph Neural Networks.

Yue Gao, Jielong Yan, Yifan Feng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 12, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Hypergraph neural networks (HGNNs) struggle with performance degradation. HGNNv2, a new hypergraph dynamic system, uses position-aware anisotropic diffusion for stable, accurate analysis of complex relational data.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Hypergraph neural networks (HGNNs) are essential for analyzing higher-order relational data.
    • HGNNs face performance degradation as network layers increase.
    • Existing hypergraph dynamic systems (HDS) lack positional information and use isotropic diffusion, limiting their precision.

    Purpose of the Study:

    • To introduce HGNNv2, a stable hypergraph neural network model.
    • To address the limitations of existing HGNNs and HDS by incorporating positional awareness and anisotropic diffusion.
    • To improve the stability and accuracy of hypergraph data analysis.

    Main Methods:

    • Developed HGNNv2 as a hypergraph dynamic system utilizing partial differential equations (PDEs).
  • Incorporated a position-aware anisotropic diffusion term and an external control term.
  • Introduced the vertex-rooted subtree method to determine anisotropic diffusion intensity.

    • Main Results:

    • HGNNv2 demonstrated superior performance across 6 hypergraph and 3 graph datasets, outperforming 12 other methods.
    • The model achieved stable final representations and task accuracy, even under noisy conditions.
    • HGNNv2 required fewer layers for stable performance compared to isotropic diffusion-based HDS.

    Conclusions:

    • HGNNv2 offers a stable and effective approach to hypergraph neural network analysis.
    • The integration of position-aware anisotropic diffusion significantly enhances information propagation and representation learning.
    • HGNNv2 represents a significant advancement in handling complex relational data with improved robustness and efficiency.