Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph property distortions and restricted structural changes persist. This leads to the question: Is it possible to develop more property-conserving and structure-sensitive augmentation methods? Through a spectral lens, we investigate the interplay between graph properties, their augmentation, and their spectral behavior, and found that keeping the low-frequency eigenvalues unchanged can preserve the critical properties at a large scale when generating augmented graphs. These observations inform our introduction of the Dual-Prism (DP) augmentation method, comprising DP-Noise and DP-Mask, which adeptly retains essential graph properties while diversifying augmented graphs. Extensive experiments validate the efficiency of our approach, providing a new and promising direction for graph data augmentation.

翻译：图神经网络（GNN）已成为处理图数据的首选工具，其有效性通过图数据增强技术得以提升。尽管增强方法不断发展，但仍存在图属性失真和结构变化受限等问题。这引出一个问题：是否可能开发出更具属性保持性和结构敏感性的增强方法？通过频谱视角，我们探究了图属性、图增强及其频谱行为之间的相互作用，并发现在生成增强图时，保持低频特征值不变能够在大尺度上保留关键属性。这些观察促使我们提出了双棱镜（DP）增强方法，包括DP-Noise和DP-Mask，该方法在增强图多样化的同时，巧妙保留了图的关键属性。大量实验验证了我们方法的有效性，为图数据增强提供了新的有前景的方向。