  Contrastive learning methods based on InfoNCE loss are popular in node
representation learning tasks on graph-structured data. However, its reliance
on data augmentation and its quadratic computational complexity might lead to
inconsistency and inefficiency problems. To mitigate these limitations, in this
paper, we introduce a simple yet effective contrastive model named Localized
Graph Contrastive Learning (Local-GCL in short). Local-GCL consists of two key
designs: 1) We fabricate the positive examples for each node directly using its
first-order neighbors, which frees our method from the reliance on
carefully-designed graph augmentations; 2) To improve the efficiency of
contrastive learning on graphs, we devise a kernelized contrastive loss, which
could be approximately computed in linear time and space complexity with
respect to the graph size. We provide theoretical analysis to justify the
effectiveness and rationality of the proposed methods. Experiments on various
datasets with different scales and properties demonstrate that in spite of its
simplicity, Local-GCL achieves quite competitive performance in self-supervised
node representation learning tasks on graphs with various scales and
properties.
