Stage-Aware Dual Alignment for Covariate Shift in Graph Domain Adaptation

The performance of Graph Domain Adaptation ( GDA ) is fundamentally limited by covariate-driven discrepancies between source and target graphs, captured by Covariate Shift ( CS ) in the joint feature-structure space beyond label-related formulations. We show that CS induces bias at two non-interchangeable stages of Graph Neural Network ( GNN ) computation: Feature Shift ( FS ) distorts representations before message passing, while Feature-Conditional Structure Shift ( FCSS ) biases propagation, rendering single-stage alignment intrinsically insufficient. Based on this, we propose Dual Alignment for Covariate Shift ( DACS ), a stage-aware GDA framework that aligns discrepancies at their source. DACS mitigates FS via adversarial feature alignment and addresses FCSS through layer-wise reweighting to correct propagation bias, followed by final adversarial alignment for residual mismatch. This design follows directly from the stage-wise structure of CS rather than heuristic combinations of alignment modules. We further show that representation and propagation biases correspond to distinct components of target-domain error that cannot be eliminated in isolation. Experiments on synthetic and real-world benchmarks demonstrate that DACS consistently outperforms prior methods, especially under complex and coupled distribution shifts.