Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively straightforward for images, but much more challenging for graphs. In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification. Instead of using uniform transformations as in existing studies, GraphAug uses an automated augmentation model to avoid compromising critical label-related information of the graph, thereby producing label-invariant augmentations at most times. To ensure label-invariance, we develop a training method based on reinforcement learning to maximize an estimated label-invariance probability. Experiments show that GraphAug outperforms previous graph augmentation methods on various graph classification tasks.

翻译：数据增强能有效提升学习机器的不变性。我们认为，数据增强的核心挑战在于设计保持标签不变的数据变换。这对于图像相对简单，但对图数据则更具挑战性。在本研究中，我们提出GraphAug——一种新颖的自动数据增强方法，旨在为图分类任务计算保持标签不变性的增强数据。与现有研究中采用统一变换不同，GraphAug使用自动增强模型来避免破坏图中关键的标签相关信息，从而在大多数情况下生成标签不变的增强数据。为确保标签不变性，我们开发了一种基于强化学习的训练方法，以最大化估计的标签不变概率。实验表明，GraphAug在各种图分类任务上均优于以往的图增强方法。