Graphs are the most common state representation for structured input problems including molecule property prediction, code representation learning and computer systems. Learning algorithms embed graph structures using graph neural networks (GNNs). However, many domains lack large training datasets due to the expense of acquiring samples; work by Mirhoseini et al. trained chip placement policies from a dataset of only 20 examples due to the complexity of designing new chips. In data-scarce settings, augmentation is widely used to improve generalization. Simple transformations like...
