Adversarial Contrastive Graph Augmentation with Counterfactual Regularization
Tao Long, Lei Zhang, Liang Zhang, Laizhong Cui
Abstract
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.
BibTeX
@article{Long_Zhang_Zhang_Cui_2025, title={Adversarial Contrastive Graph Augmentation with Counterfactual Regularization}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34101}, DOI={10.1609/aaai.v39i18.34101}, abstractNote={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.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Long, Tao and Zhang, Lei and Zhang, Liang and Cui, Laizhong}, year={2025}, month={Apr.}, pages={19086-19094} }