Class Centroid-Guided Alignment for Multi-View Cross Domain Graphs

Multi-view graph domain adaptation (MGDA) addresses label scarcity by transferring knowledge from a labeled source graph to an unlabeled target graph, where nodes are connected through multiple views. Most existing methods align each view as a whole and ignore class-level structure, so that classes can remain misaligned across domains even when view-level distributions match. We empirically reveal that the cross-domain discrepancy in class-level structure is strongly associated with alignment quality. To capture this structure, we use each view's class centroids as anchors and leverage the Cross-Domain Aggregated Class Distance (CD-ACD), which compares source and target inter-class centroid distances within each view and quantifies each view's class-level structural shift. Based on CD-ACD, we propose Class Centroid-Guided Alignment (CCGA), which uses the metric to weight the alignment of each view and class pair during training and thereby reduces class-level structural shifts between domains in each view. Experimental results on various multi-view graph benchmark datasets demonstrate the effectiveness of our method.