Randomized Schur Complement Views for Graph Contrastive Learning
This work addresses the challenge of improving graph representation learning for tasks like node and graph classification, though it appears incremental as it builds on existing GCL frameworks with a novel augmentor.
The paper tackles the problem of generating effective augmented views for Graph Contrastive Learning by introducing a randomized topological augmentor based on Schur complements, which consistently outperforms existing methods and achieves state-of-the-art results on node and graph classification benchmarks.
We introduce a randomized topological augmentor based on Schur complements for Graph Contrastive Learning (GCL). Given a graph laplacian matrix, the technique generates unbiased approximations of its Schur complements and treats the corresponding graphs as augmented views. We discuss the benefits of our approach, provide theoretical justifications and present connections with graph diffusion. Unlike previous efforts, we study the empirical effectiveness of the augmentor in a controlled fashion by varying the design choices for subsequent GCL phases, such as encoding and contrasting. Extensive experiments on node and graph classification benchmarks demonstrate that our technique consistently outperforms pre-defined and adaptive augmentation approaches to achieve state-of-the-art results.