Universal Graph Contrastive Learning with a Novel Laplacian Perturbation

Taewook Ko, Yoonhyuk Choi, Chong-Kwon Kim

Keywords: Graph Contrastive Learning, Magnetic Laplacian, Spectral Convolution

TL;DR: We introcued a novel universal graph contrastive learning by defining a Laplacian peturbation.

Abstract: Graph Contrastive Learning (GCL) is an effective method for discovering meaningful patterns in graph data. By evaluating diverse augmentations of the graph, GCL learns discriminative representations and provides a flexible and scalable mechanism for various graph mining tasks. This paper proposes a novel contrastive learning framework by introducing Laplacian perturbation. The proposed framework offers a distinct advantage by employing an indirect perturbation method, which provides a more stable approach while maintaining the perturbation effects. Moreover, it exhibits a wide range of applicability by not being restricted to specific graph types. We demonstrate that a spectral graph convolution based on the Laplacian successfully extracts representations from diverse graph types. Our extensive experiments on a variety of real-world datasets, covering multiple graph types, show that the proposed model outperforms state-of-the-art baselines in both node classification and link sign prediction tasks.