Contrastive learning has emerged as a premier method for learning
representations with or without supervision. Recent studies have shown its
utility in graph representation learning for pre-training. Despite successes,
the understanding of how to design effective graph augmentations that can
capture structural properties common to many different types of downstream
graphs remains incomplete. We propose a set of well-motivated graph
transformation operations derived via graph spectral analysis to provide a bank
of candidates when constructing augmentations for a graph contrastive
objective, enabling contrastive learning to capture useful structural
representation from pre-training graph datasets. We first present a spectral
graph cropping augmentation that involves filtering nodes by applying
thresholds to the eigenvalues of the leading Laplacian eigenvectors. Our second
novel augmentation reorders the graph frequency components in a structural
Laplacian-derived position graph embedding. Further, we introduce a method that
leads to improved views of local subgraphs by performing alignment via global
random walk embeddings. Our experimental results indicate consistent
improvements in out-of-domain graph data transfer compared to state-of-the-art
graph contrastive learning methods, shedding light on how to design a graph
learner that is able to learn structural properties common to diverse graph
types.

本文中提出了一种基于对称拉普拉斯特征值的图形裁剪增强方法和通过全局随机游走的局部子图对齐来改进图对比学习方法，并在不同类型的图表现中体现出更好的性能。