DSBD distills a dual-aligned structural basis to adapt GNNs across graphs with structural distribution shifts, outperforming prior methods on benchmarks.
Degree-conscious spiking graph for cross-domain adaptation
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DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
CORE decouples site confounders in fMRI networks, profiles transient dynamics on a population scaffold using line graphs, and applies subject-adaptive gating to achieve up to 6.7% better cross-site generalization on ABIDE, REST-meta-MDD, SRPBS, and ABCD datasets.
citing papers explorer
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DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
DSBD distills a dual-aligned structural basis to adapt GNNs across graphs with structural distribution shifts, outperforming prior methods on benchmarks.
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DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
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When Brain Networks Travel: Learning Beyond Site
CORE decouples site confounders in fMRI networks, profiles transient dynamics on a population scaffold using line graphs, and applies subject-adaptive gating to achieve up to 6.7% better cross-site generalization on ABIDE, REST-meta-MDD, SRPBS, and ABCD datasets.