AI RESEARCH
Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation
arXiv CS.LG
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ArXi:2602.10489v2 Announce Type: replace Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs but is challenged by complex, multi-faceted distributional shifts. Existing methods attempt to reduce distributional shifts by aligning manually selected graph elements (e.g., node attributes or structural statistics), which typically require manually designed graph filters to extract relevant features before alignment.