  Data augmentation helps neural networks generalize better by enlarging the
training set, but it remains an open question how to effectively augment graph
data to enhance the performance of GNNs (Graph Neural Networks). While most
existing graph regularizers focus on manipulating graph topological structures
by adding/removing edges, we offer a method to augment node features for better
performance. We propose FLAG (Free Large-scale Adversarial Augmentation on
Graphs), which iteratively augments node features with gradient-based
adversarial perturbations during training. By making the model invariant to
small fluctuations in input data, our method helps models generalize to
out-of-distribution samples and boosts model performance at test time. FLAG is
a general-purpose approach for graph data, which universally works in node
classification, link prediction, and graph classification tasks. FLAG is also
highly flexible and scalable, and is deployable with arbitrary GNN backbones
and large-scale datasets. We demonstrate the efficacy and stability of our
method through extensive experiments and ablation studies. We also provide
intuitive observations for a deeper understanding of our method.
