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Improving out-of-distribution generalization in graphs via hierarchical semantic environments
Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts.
Triangular Contrastive Learning on Molecular Graphs
Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances.
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