Graph Out of Distribution

Graph out-of-distribution (OOD) generalization focuses on improving the performance of graph neural networks (GNNs) when the test data differs significantly from the training data. Current research emphasizes developing GNNs that are robust to distribution shifts by identifying invariant features across different graph distributions, often employing techniques like contrastive learning, information bottleneck methods, and causal inference to disentangle spurious correlations from true causal relationships. This is a crucial area because reliable performance in real-world applications, where data distributions are rarely stationary, hinges on addressing the challenges posed by OOD scenarios. Improved OOD generalization in GNNs will have significant impact across various domains relying on graph-structured data, such as knowledge graph reasoning and drug discovery.