Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that GDA is an important issue for graph contrastive learning. Various GDAs have been developed recently which mainly involve dropping or perturbing edges, nodes, node attributes and edge attributes. However, to our knowledge, it still lacks a universal and effective augmentor that is suitable for different types of graph data. To address this issue, in this paper, we first introduce the graph message representation of graph data. Based on it, we then propose a novel Graph Message Augmentation (GMA), a universal scheme for reformulating many existing GDAs. The proposed unified GMA not only gives a new perspective to understand many existing GDAs but also provides a universal and more effective graph data augmentation for graph self-supervised learning tasks. Moreover, GMA introduces an easy way to implement the mixup augmentor which is natural for images but usually challengeable for graphs. Based on the proposed GMA, we then propose a unified graph contrastive learning, termed Graph Message Contrastive Learning (GMCL), that employs attribution-guided universal GMA for graph contrastive learning. Experiments on many graph learning tasks demonstrate the effectiveness and benefits of the proposed GMA and GMCL approaches.

翻译：图对比学习通常先进行图数据增强（GDA），再利用对比学习流程训练图神经网络。众所周知，GDA是图对比学习的关键问题。近年来已有多种GDA方法被提出，主要涉及对边、节点、节点属性和边属性的删除或扰动。然而，据我们所知，目前仍缺乏一种适用于不同类型图数据的通用且有效的增强方法。为解决该问题，本文首先引入图数据的图消息表示，进而提出一种新型的图消息增强（GMA）通用框架，可重构多种现有GDA方法。所提出的统一GMA不仅为理解现有GDA提供了新视角，还为图自监督学习任务提供了通用且更有效的图数据增强方法。此外，GMA为混合增强方法提供了简便实现，这类方法在图像领域天然适用，但对图数据通常具有挑战性。基于所提出的GMA，我们进一步提出了统一图对比学习框架——图消息对比学习（GMCL），该框架采用归因引导的通用GMA进行图对比学习。在多项图学习任务上的实验证明了所提出的GMA和GMCL方法的有效性与优势。