面向标签噪声下可信赖的超图神经网络
Towards Trustworthy Hypergraph Neural Networks under Label Noise
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中文总结 AI 辅助
本文针对标签噪声下超图节点分类问题，提出超图鲁棒框架HyperTrust，通过估计超边可信赖性及两个协同模块优化超图结构，在多数据集多噪声设置下验证了其有效性。
中文摘要 AI 辅助
超图神经网络（HGNNs）在处理复杂高阶关系方面展现出卓越能力，但其性能高度依赖标注数据，易受标签噪声影响。尽管带标签噪声的学习（LLN）和带标签噪声的图学习（GLN）已取得进展，但超图上的带噪标签学习仍未得到充分探索。本文对标签噪声下的超图节点分类展开系统研究：首先将代表性LLN和GLN方法适配到超图，并在统一基准下评估，揭示现有超图鲁棒学习策略的局限性；在此基础上，提出新的超图鲁棒框架HyperTrust，该框架先通过基于预训练的感知熵策略估计超边可信赖性，再集成HyperedgeBoost模块，通过将未标注节点连接到可信赖超边来增强可靠监督，同时集成HyperedgePrune模块，通过移除不可信赖的节点-超边关联来抑制噪声传播；最后两个模块协同调整超图结构并生成最终预测。大量实验和理论分析表明，HyperTrust在多种噪声设置下的多个超图数据集上具有有效性和鲁棒性，本文工作为带标签噪声的超图学习提供了统一基准和有效解决方案，为该方向的未来研究奠定了基础。
英文摘要
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 (GLN), noisy-label learning on hypergraphs remains underexplored. In this paper, we present a systematic study of hypergraph node classification under label noise. First, we adapt representative LLN and GLN methods to hypergraphs and evaluate them under a unified benchmark, revealing the limitations of existing robust learning strategies for hypergraphs. Building on this, we propose a new hypergraph robust framework, HyperTrust, which first estimates hyperedge trustworthiness through a pretraining-based, entropy-aware strategy, and then incorporates the HyperedgeBoost module to enhance reliable supervision by connecting unlabeled nodes to trustworthy hyperedges, as well as the HyperedgePrune module to suppress noisy propagation by removing untrustworthy node-hyperedge incidences. Finally, two modules work collaboratively to adjust the hypergraph structure and generate final predictions. Extensive experiments and theoretical analysis demonstrate the effectiveness and robustness of HyperTrust on multiple hypergraph datasets under various noisy settings. Our work provides a unified benchmark and an effective solution for hypergraph learning with label noise and lays a foundation for future research in this direction.
发表机构
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
- University of Chinese Academy of Sciences（中国科学院大学）
- School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）
- School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。
