Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning (GCL), by training GNNs to maximize the correspondence between the representations of the same graph in its different augmented forms, may yield robust and transferable GNNs even without using labels. However, GNNs trained by traditional GCL often risk capturing redundant graph features and thus may be brittle and provide sub-par performance in downstream tasks. Here, we propose a novel principle, termed adversarial-GCL (AD-GCL), which enables GNNs to avoid capturing redundant information during the training by optimizing adversarial graph augmentation strategies used in GCL. We pair AD-GCL with theoretical explanations and design a practical instantiation based on trainable edge-dropping graph augmentation. We experimentally validate AD-GCL by comparing with the state-of-the-art GCL methods and achieve performance gains of up-to $14\%$ in unsupervised, $6\%$ in transfer, and $3\%$ in semi-supervised learning settings overall with 18 different benchmark datasets for the tasks of molecule property regression and classification, and social network classification.

中文翻译：

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对抗性图增强以改善图对比学习

由于现实世界的图/网络数据中普遍存在标签稀缺问题，因此非常需要图神经网络 (GNN) 的自监督学习。图对比学习 (GCL) 通过训练 GNN 以最大化相同图在不同增强形式中的表示之间的对应关系，即使不使用标签也可以产生稳健且可转移的 GNN。然而，由传统 GCL 训练的 GNN 经常冒着捕获冗余图特征的风险，因此可能很脆弱，并且在下游任务中提供低于标准的性能。在这里，我们提出了一种新的原理，称为对抗性 GCL (AD-GCL)，它使 GNN 能够通过优化 GCL 中使用的对抗性图增强策略来避免在训练期间捕获冗余信息。我们将 AD-GCL 与理论解释相结合，并基于可训练的边缘下降图增强设计了一个实用的实例。我们通过与最先进的 GCL 方法进行比较来实验验证 AD-GCL，并在无监督中实现了高达 $14\%$、在转移中 $6\%$ 和半监督中 $3\%$ 的性能提升使用 18 个不同的基准数据集整体学习设置，用于分子属性回归和分类以及社交网络分类的任务。