  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.
