AI RESEARCH
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
arXiv CS.LG
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ArXi:2604.03154v1 Announce Type: new 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 feature-centric and overlook structural discrepancies, which become particularly detrimental under significant topology shifts. Such discrepancies alter both geometric relationships and spectral properties, leading to unreliable transfer of graph neural networks (GNNs