Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation

2024-12-16

AI Summary

Unsupervised Graph Domain Adaptation (UGDA) suffers from GNN knowledge transfer failure due to structural distribution shift across domains, especially degrading severely when the target graph exhibits high topological complexity. To address this, we propose Target-Domain-oriented Structural Smoothing (TDSS), the first method in UGDA to explicitly incorporate structural smoothing—jointly optimizing neighborhood sampling and target-graph topology to preserve structural consistency while mitigating over-smoothing. We theoretically establish that TDSS reduces the target-domain generalization risk and enhances model smoothness. Extensive experiments across three real-world datasets and six cross-domain transfer scenarios demonstrate consistent and significant improvements over state-of-the-art methods. Our implementation is publicly available.

Technology Category

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

Application Category

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

Abstract

Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graphs. Existing UGDA methods primarily focus on aligning features in the latent space learned by graph neural networks (GNNs) across domains, often overlooking structural shifts, resulting in limited effectiveness when addressing structurally complex transfer scenarios. Given the sensitivity of GNNs to local structural features, even slight discrepancies between source and target graphs could lead to significant shifts in node embeddings, thereby reducing the effectiveness of knowledge transfer. To address this issue, we introduce a novel approach for UGDA called Target-Domain Structural Smoothing (TDSS). TDSS is a simple and effective method designed to perform structural smoothing directly on the target graph, thereby mitigating structural distribution shifts and ensuring the consistency of node representations. Specifically, by integrating smoothing techniques with neighborhood sampling, TDSS maintains the structural coherence of the target graph while mitigating the risk of over-smoothing. Our theoretical analysis shows that TDSS effectively reduces target risk by improving model smoothness. Empirical results on three real-world datasets demonstrate that TDSS outperforms recent state-of-the-art baselines, achieving significant improvements across six transfer scenarios. The code is available in https://github.com/cwei01/TDSS.

Problem

• Unsupervised Graph Domain Adaptation
• Graph Neural Networks
• Structural Discrepancy

Innovation

• Target Domain Structure Smoothing (TDSS)
• Unsupervised Graph Domain Adaptation (UGDA)
• Over-smoothing Avoidance