Graph contrastive learning (GCL), learning the node representation by
contrasting two augmented graphs in a self-supervised way, has attracted
considerable attention. GCL is usually believed to learn the invariant
representation. However, does this understanding always hold in practice? In
this paper, we first study GCL from the perspective of causality. By analyzing
GCL with the structural causal model (SCM), we discover that traditional GCL
may not well learn the invariant representations due to the non-causal
information contained in the graph. How can we fix it and encourage the current
GCL to learn better invariant representations? The SCM offers two requirements
and motives us to propose a novel GCL method. Particularly, we introduce the
spectral graph augmentation to simulate the intervention upon non-causal
factors. Then we design the invariance objective and independence objective to
better capture the causal factors. Specifically, (i) the invariance objective
encourages the encoder to capture the invariant information contained in causal
variables, and (ii) the independence objective aims to reduce the influence of
confounders on the causal variables. Experimental results demonstrate the
effectiveness of our approach on node classification tasks.

本文从因果关系的角度研究了图对比学习（Graph Contrastive Learning），发现传统的图对比学习由于含有非因果信息而不能很好地学习不变表示。为了解决这个问题并促使当前的图对比学习方法学习更好的不变表示，我们提出了一种新的图对比学习方法，通过引入谱图增强来模拟对非因果因素的干预，并设计了不变性目标和独立性目标来更好地捕捉因果因素。实验结果表明我们的方法在节点分类任务上的有效性。