Rethinking Graph Domain Adaptation: A Spectral Contrastive Perspective

2025-10-15

AI Summary

Graph Neural Networks (GNNs) suffer performance degradation in cross-domain scenarios due to structural distribution shifts, while existing domain adaptation methods struggle to simultaneously preserve global invariance and local structural details. To address this, we propose the first spectral-analysis-based graph domain adaptation framework, termed Frequency-Aware Contrastive Learning (FACL). Our method leverages Graph Fourier Transform to decouple low-frequency components (capturing global, domain-invariant structures) from high-frequency components (encoding local, domain-specific details). We design a dual-branch architecture—low-pass and high-pass filters—to separately process and align these frequency subspaces. Contrastive learning is then applied within each branch to enhance domain discriminability and promote intra-class compactness across domains. We provide theoretical analysis establishing improved generalization bounds under structural distribution shift. Extensive experiments on multiple benchmark datasets demonstrate that FACL significantly outperforms state-of-the-art methods, validating its effectiveness in mitigating structural distribution shift and improving cross-domain transfer performance.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Social Networks

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Graph neural networks (GNNs) have achieved remarkable success in various domains, yet they often struggle with domain adaptation due to significant structural distribution shifts and insufficient exploration of transferable patterns. One of the main reasons behind this is that traditional approaches do not treat global and local patterns discriminatingly so that some local details in the graph may be violated after multi-layer GNN. Our key insight is that domain shifts can be better understood through spectral analysis, where low-frequency components often encode domain-invariant global patterns, and high-frequency components capture domain-specific local details. As such, we propose FracNet (underline{ extbf{Fr}}equency underline{ extbf{A}}ware underline{ extbf{C}}ontrastive Graph underline{ extbf{Net}}work) with two synergic modules to decompose the original graph into high-frequency and low-frequency components and perform frequency-aware domain adaption. Moreover, the blurring boundary problem of domain adaptation is improved by integrating with a contrastive learning framework. Besides the practical implication, we also provide rigorous theoretical proof to demonstrate the superiority of FracNet. Extensive experiments further demonstrate significant improvements over state-of-the-art approaches.

Problem

• Addressing structural distribution shifts in graph domain adaptation
• Distinguishing global and local patterns via spectral analysis
• Improving adaptation through frequency decomposition and contrastive learning

Innovation

• Decomposes graphs into high and low frequency components
• Uses frequency-aware contrastive learning for domain adaptation
• Integrates spectral analysis with graph neural networks