CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization
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 mechanisms, leading to weak OOD robustness and unstable predictive representations. Existing solutions based on environment invariance or causal reasoning are insufficient for node classification, as they do not explicitly model the fine-grained latent geometry required by the task. We further observe a training instability named Info-Jitter, where the mutual information between predictive representations and ground-truth labe...
| Authors | Bowen Lu, Lianqiang Yang, Teng Li et al. |
|---|---|
| Primary category | stat.ML |
| Published | 25 March 2026 |
| Last updated at source | 12 August 2026 |
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