用高斯混合模型增强图数据，提升GNN在未知数据上的泛化能力
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation
- 基于径向复杂度理论设计增广框架，指导有效数据增强
- 在有限或单一数据下显著提升GNN对未知数据的泛化性能
- 新方法高效且适合真实场景，兼顾效果与计算效率
图神经网络（GNN）在节点和图分类等任务中表现优异，但在面对未见或分布外（OOD）数据时泛化能力差，尤其在训练数据量小或多样性不足时更明显。为此，本文提出一个基于Rademacher复杂度的理论框架，推导出泛化误差的遗憾上界，并分析数据增强的影响。该框架指导设计了GRATIN算法，利用高斯混合模型（GMM）逼近任意分布的能力，实现高效图数据增广。实验表明，该方法在泛化性能上优于现有技术，同时具备更优的时间复杂度，适用于实际应用。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribution (OOD) data. These challenges are exacerbated when training data is limited in size or diversity. To address these issues, we introduce a theoretical framework using Rademacher complexity to compute a regret bound on the generalization error and then characterize the effect of data augmentation. This framework informs the design of GRATIN, an efficient graph data augmentation algorithm leveraging the capability of Gaussian Mixture Models (GMMs) to approximate any distribution. Our approach not only outperforms existing augmentation techniques in terms of generalization but also offers improved time complexity, making it highly suitable for real-world applications.
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