Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification

• URL: http://arxiv.org/abs/2401.09953v2
• Date: Mon, 22 Jan 2024 08:32:02 GMT
• Title: Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
• Authors: Yutong Xia, Runpeng Yu, Yuxuan Liang, Xavier Bresson, Xinchao Wang, Roger Zimmermann
• Abstract summary: We introduce the Dual-Prism (DP) augmentation method, comprising DP-Noise and DP-Mask. We find that keeping the low-frequency eigenvalues unchanged can preserve the critical properties at a large scale when generating augmented graphs.
• Score: 71.36575018271405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph property distortions and restricted structural changes persist. This leads to the question: Is it possible to develop more property-conserving and structure-sensitive augmentation methods? Through a spectral lens, we investigate the interplay between graph properties, their augmentation, and their spectral behavior, and found that keeping the low-frequency eigenvalues unchanged can preserve the critical properties at a large scale when generating augmented graphs. These observations inform our introduction of the Dual-Prism (DP) augmentation method, comprising DP-Noise and DP-Mask, which adeptly retains essential graph properties while diversifying augmented graphs. Extensive experiments validate the efficiency of our approach, providing a new and promising direction for graph data augmentation.

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