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Preprint Aug 2026

Towards Trustworthy Hypergraph Neural Networks under Label Noise

Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on labeled data, making them vulnerable to label noise. Despite advances in learning with label noise (LLN) and graph learning with label noise (G...

Meng-Yao Zhou, Zhi-Heng Zhou, Xiao Han et al. · 0 citations

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