Adversarial Contrastive Graph Augmentation with Counterfactual Regularization

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positive graph augmentations, they often overlook the importance of negative samples, relying instead on rule-based methods that can fail to capture meaningful graph patterns. To address this issue, we propose a novel model-based adversarial contrastive graph augmentation (ACGA) method that automatically generates both positive graph samples with minimal sufficient information and hard negative graph samples. Additionally, we provide a theoretical framework to analyze the process of positive and negative graph augmentation in self-supervised GCL. We evaluate our ACGA method through extensive experiments on representative benchmark datasets, and the results demonstrate that ACGA outperforms state-of-the-art baselines.