  Existing graph contrastive learning (GCL) techniques typically require two
forward passes for a single instance to construct the contrastive loss, which
is effective for capturing the low-frequency signals of node features. Such a
dual-pass design has shown empirical success on homophilic graphs, but its
effectiveness on heterophilic graphs, where directly connected nodes typically
have different labels, is unknown. In addition, existing GCL approaches fail to
provide strong performance guarantees. Coupled with the unpredictability of GCL
approaches on heterophilic graphs, their applicability in real-world contexts
is limited. Then, a natural question arises: Can we design a GCL method that
works for both homophilic and heterophilic graphs with a performance guarantee?
To answer this question, we theoretically study the concentration property of
features obtained by neighborhood aggregation on homophilic and heterophilic
graphs, introduce the single-pass augmentation-free graph contrastive learning
loss based on the property, and provide performance guarantees for the
minimizer of the loss on downstream tasks. As a direct consequence of our
analysis, we implement the Single-Pass Graph Contrastive Learning method
(SP-GCL). Empirically, on 14 benchmark datasets with varying degrees of
homophily, the features learned by the SP-GCL can match or outperform existing
strong baselines with significantly less computational overhead, which
demonstrates the usefulness of our findings in real-world cases.
