Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

Graph contrastive learning (GCL) aims to learn representations by capturing the agreements between different graph views. Traditional GCL methods generate views in the spatial domain, but it has been recently discovered that the spectral domain also plays a vital role in complementing spatial views. However, existing spectral-based graph views either ignore the eigenvectors that encode valuable positional information, or suffer from high complexity when trying to address the instability of spectral features. To tackle these challenges, we first design an informative, stable, and scalable spectral encoder, termed EigenMLP, to learn effective representations from the spectral features. Theoretically, EigenMLP is invariant to the rotation and reflection transformations on eigenvectors and robust against perturbations. Then, we propose a spatial-spectral contrastive framework (Sp2GCL) to capture the consistency between the spatial information encoded by graph neural networks and the spectral information learned by EigenMLP, thus effectively fusing these two graph views. Experiments on the node- and graph-level datasets show that our method not only learns effective graph representations but also achieves a 2–10x speedup over other spectral-based methods.

Keywords

Graph contrastive learning, spectral encoding, spatial-spectral graph neural networks

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, December 10-16

First Page

1

Last Page

17

City or Country

New Orleans, United States

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