AAGCN: An adaptive data augmentation for graph contrastive learning

Abstract

Contrastive learning has achieved great success in many applications. A key step in contrastive learning is to find a positive sample and negative samples. Traditional methods find the positive sample by choosing the most similar sample. A more popular approach is to do data augmentation where the original data and the augmented data are naturally treated as positive pairs. It is easy for grid data to do the augmentation, for example, we can rotate, crop or color an image to get augmented images. But it is challenging to do augmentation for graph data, due to non-Euclidean nature of graphs. Current graph augmentation methods mainly focus on masking nodes, dropping edges, or extracting subgraphs. Such methods lack of flexibility and require intensive manual settings. In this work, we propose a model called Adaptive Augmentation Graph Convolutional Network (AAGCN) for semi-supervised node classification, based on adaptive graph augmentation. Rather than choose a probability distribution, for example, Bernoulli distribution, to drop some of the nodes or edges in Dropout, the proposed model learns the mask matrices for nodes or edges adaptively. Experiments on citation networks such as Cora, CiteSeer and Cora-ML show that AAGCN achieved state-of-the-art performance compared with other popular graph neural networks. The proposed model was also tested on a more challenging and large-scale graph dataset, OGBN-Arxiv, which has 169,343 nodes and 1,166,243 edges. The proposed model could still achieve competitive prediction results.