arXiv stat.ML
· Papers
CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization
arXiv:2603.24304v3 Announce Type: replace Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios. Under distribution shifts, GNNs often fit environmental noise and spurious correlations instead of stable causal m