Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily relies on graph augmentation to reflect invariant patterns that are robust to small perturbations; yet it still remains unclear about what graph invariance GCL should capture. Recent studies mainly perform topology augmentations in a uniformly random manner in the spatial domain, ignoring its influence on the intrinsic structural properties embedded in the spectral domain. In this work, we aim to find a principled way for topology augmentations by exploring the invariance of graphs from the spectral perspective. We develop spectral augmentation which guides topology augmentations by maximizing the spectral change. Extensive experiments on both graph and node classification tasks demonstrate the effectiveness of our method in self-supervised representation learning. The proposed method also brings promising generalization capability in transfer learning, and is equipped with intriguing robustness property under adversarial attacks. Our study sheds light on a general principle for graph topology augmentation.

翻译：图对比学习（Graph Contrastive Learning, GCL）作为一种新兴的图自监督学习技术，旨在通过实例判别学习表示。其性能高度依赖于图增强方法，以反映对微小扰动鲁棒的恒等模式，但图对比学习应捕捉何种图恒等性仍不明确。近期研究主要在空间域中采用均匀随机方式进行拓扑增强，忽略了其对谱域中固有结构特性的影响。本文旨在从谱视角探索图的恒等性，提出一种有原则的拓扑增强方法。我们开发了谱增强（Spectral Augmentation），通过最大化谱变化来指导拓扑增强。在图和节点分类任务上的大量实验证明了该方法在自监督表示学习中的有效性。所提方法在迁移学习中也展现出良好的泛化能力，并在对抗攻击下具有引人注目的鲁棒性。本研究为图拓扑增强揭示了一般性原则。