Spectral Augmentation for Self-Supervised Learning on Graphs

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 unsupervised learning, as well as the generalization capability in transfer learning and the robustness property under adversarial attacks. Our study sheds light on a general principle for graph topology augmentation. Most existing works perform topology augmentations in a uniformly random manner (Zhu et al., 2020; Thakoor et al., 2021) , which achieves a certain level of empirical success, but is far from optimal: