用高阶超图建模分子，提升大分子预测精度
EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks
- 设计旋转等变超图神经网络，捕捉分子多体相互作用
- 在大分子数据集上性能超越传统图模型，小分子提升有限
- 适合研究复杂分子系统或需几何信息的科学计算人群
分子相互作用常涉及高阶关系，传统图模型仅能表达成对连接，难以全面刻画。超图通过支持多体交互自然扩展了图结构，更适于建模复杂分子体系。本文提出 EquiHGNN，一种具有对称性感知的旋转等变超图神经网络框架，通过在相关变换群下保持等变性，有效保留几何与拓扑特性，生成更鲁棒且物理意义明确的表示。我们评估多种等变架构，结果表明引入对称性约束可显著提升大规模分子数据集上的性能。小分子上高阶交互收益有限，但在大分子上持续优于二维图模型。进一步融合几何特征可提升表现，凸显空间信息在分子学习中的价值。代码已开源。
原文摘要 · Abstract (English)
Molecular interactions often involve high-order relationships that cannot be fully captured by traditional graph-based models limited to pairwise connections. Hypergraphs naturally extend graphs by enabling multi-way interactions, making them well-suited for modeling complex molecular systems. In this work, we introduce EquiHGNN, an Equivariant HyperGraph Neural Network framework that integrates symmetry-aware representations to improve molecular modeling. By enforcing the equivariance under relevant transformation groups, our approach preserves geometric and topological properties, leading to more robust and physically meaningful representations. We examine a range of equivariant architectures and demonstrate that integrating symmetry constraints leads to notable performance gains on large-scale molecular datasets. Experiments on both small and large molecules show that high-order interactions offer limited benefits for small molecules but consistently outperform 2D graphs on larger ones. Adding geometric features to these high-order structures further improves the performance, emphasizing the value of spatial information in molecular learning. Our source code is available at https://github.com/HySonLab/EquiHGNN/
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