Data augmentation helps neural networks generalize better, 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 augmenting graph topological structures by adding/removing edges, we offer a novel direction to augment in the input node feature space for better performance. We propose a simple but effective solution, FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training, and boosts performance at test time. Empirically, FLAG can be easily implemented with a dozen lines of code and is flexible enough to function with any GNN backbone, on a wide variety of large-scale datasets, and in both transductive and inductive settings. Without modifying a model's architecture or training setup, FLAG yields a consistent and salient performance boost across both node and graph classification tasks. Using FLAG, we reach state-of-the-art performance on the large-scale ogbg-molpcba, ogbg-ppa, and ogbg-code datasets.

本文提出基于对抗扰动的方法，通过扩充图数据中的节点特征来提高GNN（Graph Neural Networks）的性能，从而使模型对输入数据的小波动具有不变性，帮助模型泛化到分布外的样本并提高性能。该方法适用于节点分类、连接预测和图分类任务，具有高度的灵活性和可扩展性，并可在任意GNN骨干结构和大规模数据集中使用。

大规模图形的数据增强鲁棒优化