Unifying Graph Contrastive Learning via Graph Message Augmentation
Ziyan Zhang Bo Jiang Jin Tang Bin Luo
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.