  Graph contrastive learning (GCL) is the most representative and prevalent
self-supervised learning approach for graph-structured data. Despite its
remarkable success, existing GCL methods highly rely on an augmentation scheme
to learn the representations invariant across different augmentation views. In
this work, we revisit such a convention in GCL through examining the effect of
augmentation techniques on graph data via the lens of spectral theory. We found
that graph augmentations preserve the low-frequency components and perturb the
middle-and high-frequency components of the graph, which contributes to the
success of GCL algorithms on homophilic graphs but hinder its application on
heterophilic graphs, due to the high-frequency preference of heterophilic data.
Motivated by this, we propose a novel, theoretically-principled, and
augmentation-free GCL method, named AF-GCL, that (1) leverages the features
aggregated by Graph Neural Network to construct the self-supervision signal
instead of augmentations and therefore (2) is less sensitive to the graph
homophily degree. Theoretically, We present the performance guarantee for
AF-GCL as well as an analysis for understanding the efficacy of AF-GCL.
Extensive experiments on 14 benchmark datasets with varying degrees of
heterophily show that AF-GCL presents competitive or better performance on
homophilic graphs and outperforms all existing state-of-the-art GCL methods on
heterophilic graphs with significantly less computational overhead.
