Barlow Twins-Guided Adaptive Alignment for Multi-View Graph Domain Adaptation

Multi-view graph domain adaptation (MGDA) transfers knowledge from a labeled source graph to an unlabeled target graph when both are observed through multiple views. Because domain shift differs across views, aligning the two graphs with a single view-agnostic objective is insufficient, yet existing methods weight all view pairs uniformly and overlook how each source view relates to its target counterpart. We characterize this relationship for every source–target view pair with two quantities: consistency and redundancy. Our empirical results reveal that domain discrepancy decreases with consistency and increases with redundancy. This suggests these two quantities can serve as both training signals and indicators of how strongly to align each pair. We therefore propose Barlow Twins-Guided Adaptive Alignment (BTGA), which applies the invariance and redundancy-reduction terms of Barlow Twins to each source–target view pair and uses the resulting consistency and redundancy scores to weight the pair's alignment. Experimental results on a variety of benchmark datasets verify the effectiveness of our method.