  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.
