mlsanityData to 14 Oct 2026

Randomized Schur Complement Views for Graph Contrastive Learning

arXiv 2306.04004 ↗First posted 6 Jun 2023cs.LG cs.SIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 66 of its 106 references that are in the map. First posted on arXiv 6 Jun 2023.

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66 of 106 references.

References · 66

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38 more
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