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cs.LG2026
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
Yingxu Wang, Kunyu Zhang, Jiaxin Huang +4
Graph domain adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph under distribution shifts. However, existing methods are largely f…
cs.LG2026
USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation
Yingxu Wang, Kunyu Zhang, Mengzhu Wang +2
SF-GDA is pivotal for privacy-preserving knowledge transfer across graph datasets. Although recent works incorporate structural information, they implicitly condition adaptation on…
cs.LG2026
DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
Yingxu Wang, Xinwang Liu, Mengzhu Wang +2
Graph Domain Adaptation (GDA) aims to transfer graph classifiers across domains with both semantic and topological shifts. Existing Euclidean adversarial methods face two challenge…