论文标题
在图表上进行自我监督学习的光谱增强
Spectral Augmentation for Self-Supervised Learning on Graphs
论文作者
论文摘要
图形对比学习（GCL）是一种在图形上的新兴自我监督学习技术，旨在通过实例歧视来学习表示形式。它的性能在很大程度上依赖于图表，以反映对小扰动的强大模式。然而，对于应捕获哪种图形不变性GCL仍然不清楚。最近的研究主要是在空间结构域中以均匀随机的方式进行拓扑增强，而忽略了其对光谱结构域中嵌入的内在结构特性的影响。在这项工作中，我们旨在通过从光谱的角度探索图形的不变性来找到一种原则上的拓扑扩展方法。我们开发光谱增强，通过最大化光谱变化来指导拓扑增强。在图和节点分类任务上进行的广泛实验证明了我们方法在自我监督的表示学习中的有效性。所提出的方法还带来了有希望的转移学习能力，并配备了在对抗攻击下具有有趣的鲁棒性特性。我们的研究阐明了图形拓扑扩展的一般原则。
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.
