Coupling Category Alignment for Graph Domain Adaptation

Nan Yin, Zhiguang Cao, houcheng su, Mengzhu Wang

Keywords: Graph domain adaptation

Abstract:

Graph domain adaptation (GDA), which transfers knowledge from a labeled source domain to an unlabeled target graph domain, attracts considerable attention in numerous fields. Emerging methods commonly employ message-passing neural networks (MPNNs) to learn domain-invariant representations by aligning the entire domain distribution. However, these methods overlook the category-level distribution alignment across different domains, potentially leading to confusion of categories. To address the problem, we propose an effective framework named \textbf{Co}upling \textbf{C}ateg{o}ry \textbf{A}lignment (\method{}) for GDA, which effectively addresses the category alignment issue with theoretical guarantees. \method{} incorporates a graph convolutional network branch and a graph kernel network branch, which explore graph topology in implicit and explicit manners. To mitigate category-level domain shifts, we leverage knowledge from both branches, iteratively filtering highly reliable samples from the target domain using one branch and fine-tuning the other accordingly. Furthermore, with these reliable target domain samples, we incorporate the coupled branches into a holistic contrastive learning framework. This framework includes multi-view contrastive learning to ensure consistent representations across the dual branches, as well as cross-domain contrastive learning to achieve category-level domain consistency. Theoretically, we establish a sharper generalization bound, which ensures the effectiveness of category alignment. Extensive experiments on benchmark datasets validate the superiority of the proposed \method{} compared with baselines.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 9347