Soft Augmentation for Graph Classification

Abstract

Graph data augmentation proves to be an effective approach for enhancing the performance of graph classification. However, due to the complex structure of graphs, the semantic meanings of graphs are sensitive to minor modifications, while the labels of augmented graphs remain identical, thus limiting the potential benefits of graph data augmentation. To address this limitation, we propose Graph Soft Augmentation (GSA), a method to smooth the labels of augmented graphs. Instead of assigning hard and fixed labels to augmented graphs as traditional graph data augmentations, which do not consider the changed semantics, GSA smooths the labels of augmented graphs. GSA can be divided into two stages. In the first stage, a graph similarity network is trained with the original dataset until convergence. In the second stage, GSA adopts a general graph data augmentation to construct augmented graphs. The labels of augmented graphs are then smoothed based on the similarities computed by the similarity network. Finally, the resulting augmented graphs, along with their smoothed labels, are incorporated into the graph classification network as training samples. Experimental results on a variety of publicly available datasets reveal the effectiveness of our GSA.