FAVA: Frequency Alignment across Views for Multiview Graph Domain Adaptation

Multiview graph domain adaptation (MGDA) transfers knowledge from a labeled source graph to an unlabeled target graph, where nodes are connected through multiple relation views. We observe on real-world graphs that the source and target domains differ in their frequency composition and that this shift differs from one relation view to another. Existing MGDA methods align each view representation as a whole, leaving frequency-specific shifts uncorrected. We propose Frequency-Aware View Alignment (FAVA), which aligns the frequency composition of relation views across domains. For each relation view, FAVA learns three channels that separately capture low-frequency information, high-frequency variation, and full-pass node attributes, so that alignment can act on each frequency component individually. It then aligns these channels across all source–target view pairs, weighting each pair by its estimated shift to accommodate the view-dependent frequency shift. Experimental results on real-world graph benchmarks demonstrate the effectiveness of our method.