Automated Data Augmentations for Graph Classification

Understand

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.