Enhancing Node-Level Graph Domain Adaptation by Alleviating Local Dependency

Recent years have witnessed significant advancements in machine learning methods on graphs. However, transferring knowledge effectively from one graph to another remains a critical challenge. This highlights the need for algorithms capable of applying information extracted from a source graph to an unlabeled target graph, a task known as unsupervised graph domain adaptation (GDA). One key difficulty in unsupervised GDA is conditional shift, which hinders transferability. In this paper, we show that conditional shift can be observed only if there exists local dependencies among node features. To support this claim, we perform a rigorous analysis and also further provide generalization bounds of GDA when dependent node features are modeled using markov chains. Guided by the theoretical findings, we propose to improve GDA by decorrelating node features, which can be specifically implemented through decorrelated GCN layers and graph transformer layers. Our experimental results demonstrate the effectiveness of this approach, showing not only substantial performance enhancements over baseline GDA methods but also clear visualizations of small intra-class distances in the learned representations. Our code is available at https://github.com/TechnologyAiGroup/DFT

💡 Research Summary

This paper presents a novel theoretical and practical framework for addressing the challenge of Unsupervised Graph Domain Adaptation (GDA). The core problem in GDA is transferring knowledge from a labeled source graph to an unlabeled target graph, which is hindered by distribution shifts, particularly conditional shift where the label distribution given the input graph structure differs between domains.

The authors make several key contributions. First, they establish a fundamental theoretical link: under the common covariate shift assumption, the presence of conditional shift necessarily implies that node features are not independently sampled but exhibit local dependencies (Theorem 3.1). This insight reframes the problem, suggesting that the inherent interdependency in graph data is a root cause of transfer difficulty.

Second, to quantify the impact of this dependency, the paper derives novel generalization bounds for GDA when node features are modeled via a Markov chain (Theorem 3.2). The bound reveals that the mixing time of the Markov chain—a measure of dependency strength—directly influences the generalization error upper bound. Stronger dependencies (longer mixing time) lead to a looser bound, theoretically explaining why dependency hinders adaptation performance. The analysis further critiques standard Graph Convolutional Networks (GCNs), showing that their message-passing scheme can amplify feature correlations, making them suboptimal backbones for GDA.

Guided by these theoretical findings, the paper proposes a principled solution: alleviating local dependency by decorrelating node features at the representation level. Two concrete neural architectures are introduced to implement this idea: 1) Decorrelated GCN Layers, which explicitly remove correlations introduced during neighborhood aggregation within each GCN layer, and 2) Graph Transformer Layers, whose self-attention mechanism can capture global context while mitigating localized dependency patterns. Both approaches incorporate a decorrelation loss term into the training objective to encourage the learning of less interdependent node representations.

Extensive experiments are conducted on standard node-level GDA benchmarks, including citation networks (DBLPv7, ACM, Citationv1) and co-author networks (Coauthor-CS, Coauthor-Physics). The proposed method, dubbed DFT (Decorrelating Feature Transfer), demonstrates substantial and consistent performance improvements over strong baselines such as UDA-GCN, AdaGCN, and GTrans. Ablation studies confirm the contribution of each proposed component. Furthermore, t-SNE visualizations of the learned representations provide empirical evidence that DFT successfully creates more discriminative feature spaces with smaller intra-class distances, aligning with the theoretical goal of reducing detrimental dependencies.

In summary, this work provides a rigorous theoretical analysis connecting conditional shift to node dependency in graphs, formalizes how such dependency harms generalization, and offers effective, architecture-agnostic techniques to decorrelate features for significantly improved unsupervised graph domain adaptation.