AI RESEARCH
CGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization
arXiv CS.LG
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ArXi:2603.24304v1 Announce Type: cross Graph Neural Networks (GNNs) have achieved impressive performance in graph-related tasks. However, they suffer from poor generalization on out-of-distribution (OOD) data, as they tend to learn spurious correlations. Such correlations present a phenomenon that GNNs fail to stably learn the mutual information between prediction representations and ground-truth labels under OOD settings.