Graph augmentation plays a crucial role in graph contrastive learning. However, existing methods primarily optimize augmentations specific to particular datasets, which limits their robustness and generalization capabilities. To overcome these limitations, many studies have explored automated graph data augmentations. However, these approaches face challenges due to weak labels and data incompleteness. To tackle these challenges, we propose an innovative framework called Joint Data Augmentations for Automated Graph Contrastive Learning (JDAGCL). The proposed model first integrates two augmenters: a feature-level augmenter and an edge-level augmenter. The two augmenters learn whether to drop an edge or node to obtain optimized graph structures and enrich the information available for modeling and forecasting task. Moreover, we introduce two stage training strategy to further process the features extracted by the encoder and enhance their effectiveness for forecasting downstream task. The experimental results demonstrate that our proposed model JDAGCL achieves state-of-the-art performance compared to the latest baseline methods, with an average improvement of 14% in forecasting accuracy across multiple benchmark datasets.

图增强在图对比学习中起着至关重要的作用。然而，现有方法主要优化特定于特定数据集的增强，这限制了它们的鲁棒性和泛化能力。为了克服这些限制，许多研究探索了自动化图形数据增强。然而，由于标签薄弱和数据不完整，这些方法面临挑战。为了应对这些挑战，我们提出了一个创新框架，称为自动图对比学习的联合数据增强（JDAGCL）。所提出的模型首先集成了两个增强器：特征​​级增强器和边缘级增强器。这两个增强器学习是否删除边或节点以获得优化的图结构并丰富可用于建模和预测任务的信息。此外，我们引入了两阶段训练策略来进一步处理编码器提取的特征并增强其预测下游任务的有效性。实验结果表明，与最新的基线方法相比，我们提出的模型 JDAGCL 实现了最先进的性能，在多个基准数据集上的预测精度平均提高了 14%。