  Graph Contrastive Learning (GCL), learning the node representations by
augmenting graphs, has attracted considerable attentions. Despite the
proliferation of various graph augmentation strategies, some fundamental
questions still remain unclear: what information is essentially encoded into
the learned representations by GCL? Are there some general graph augmentation
rules behind different augmentations? If so, what are they and what insights
can they bring? In this paper, we answer these questions by establishing the
connection between GCL and graph spectrum. By an experimental investigation in
spectral domain, we firstly find the General grAph augMEntation (GAME) rule for
GCL, i.e., the difference of the high-frequency parts between two augmented
graphs should be larger than that of low-frequency parts. This rule reveals the
fundamental principle to revisit the current graph augmentations and design new
effective graph augmentations. Then we theoretically prove that GCL is able to
learn the invariance information by contrastive invariance theorem, together
with our GAME rule, for the first time, we uncover that the learned
representations by GCL essentially encode the low-frequency information, which
explains why GCL works. Guided by this rule, we propose a spectral graph
contrastive learning module (SpCo), which is a general and GCL-friendly
plug-in. We combine it with different existing GCL models, and extensive
experiments well demonstrate that it can further improve the performances of a
wide variety of different GCL methods.
