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• W3135425306 abstract "In general, graph representation learning methods assume that the train and test data come from the same distribution. In this work we consider an underexplored area of an otherwise rapidly developing field of graph representation learning: The task of out-of-distribution (OOD) graph classification, where train and test data have different distributions, with test data unavailable during training. Our work shows it is possible to use a causal model to learn approximately invariant representations that better extrapolate between train and test data. Finally, we conclude with synthetic and real-world dataset experiments showcasing the benefits of representations that are invariant to train/test distribution shifts." @default.
• W3135425306 created "2021-03-15" @default.
• W3135425306 creator A5031236820 @default.
• W3135425306 creator A5035917702 @default.
• W3135425306 creator A5037950322 @default.
• W3135425306 date "2021-03-08" @default.
• W3135425306 modified "2023-09-23" @default.
• W3135425306 title "Size-Invariant Graph Representations for Graph Classification Extrapolations" @default.
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