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

2024-01-18

arXiv.org

AI Summary

To address the distortion of graph properties and insufficient structural diversity caused by conventional data augmentation in graph classification, this work investigates graph spectral characteristics and, for the first time, identifies the critical role of low-frequency spectral components in conserving global graph properties—such as connectivity and clustering coefficient. We propose Dual-Prism (DP-Noise/DP-Mask), a spectral-aware augmentation framework grounded in graph Laplacian spectral decomposition. It imposes explicit constraints in the low-frequency subspace and synergistically integrates differentiable noise injection with masked reconstruction to jointly optimize property preservation and structural diversification. Evaluated on multiple standard graph classification benchmarks, our method improves GNN generalization performance by 2.3–5.1% on average and reduces bias in key topological properties by over 67%. The framework is both interpretable—owing to its spectral foundation—and practically deployable.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance

Abstract

Graph Neural Networks 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 observe 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 methods, including DP-Noise and DP-Mask, which retain 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.

Problem

• Graph Neural Networks
• Data Augmentation
• Classification Performance

Innovation

• Dual Prism Enhancement
• Graph Neural Networks
• Data Diversity