Graph Out-of-distribution Generalization.pdf

1、DataFunSummit#2023Graph Out-of-Distribution Generalization From a Causal Perspective演讲人：隋勇铎(Yongduo Sui)中国科学技术大学博士生蚂蚁集团实习生01Background and Motivation03Causal Attention Learning02Related Studies04Adversarial Invariant AugmentationCONTENTDataFunSummit#202301Background and Motivation1.1 Background Grap

2、h data are everywhere Social network Chemical molecule Biological proteinSocial networkMoleculeProtein structure Node classification Link prediction/classification Graph classification Graph learning tasks1.2 Graph Out-of-distribution Issue OOD Issue in Image Classification1 OoD-Bench:Quantifying an

3、d Understanding Two Dimensions of Out-of-Distribution Generalization,CVPR 2022Covariate shiftCorrelation shiftCow&Camel1.2 Graph Out-of-distribution Issue OOD Issue in Graph Classification3 OOD-GNN:Out-of-Distribution Generalized Graph Neural Network,TKDE 20222 Discovering Invariant Rationales for G

4、raph Neural Networks,ICLR 20221.3 Assumption of Graph Generation Stable(Causal)&Environmental Feature Sufficiency&Invariance Assumption Molecule:CyclopropanolScaffold:3-carbon ringMolecule:1,4-cyclohexanediol Scaffold:6-carbon ringStable feature:functional group,e.g.OH,-COOHEnvironmental feature:sca

5、ffold,e.g.carbon ring,carbon chainMolecule:acetic acidScaffold:small sizeMolecule:citric acidScaffold:large size4 Learning Invariant Graph Representations for Out-of-Distribution Generalization,NeurIPS 20225 Learning Substructure Invariance for Out-of-Distribution Molecular Representations,NeurIPS 2

6、0221.4 Our Motivations Possible reasons for poor performance of GNNs on OOD test dataStable features are difficult to captureEnvironmental features are not discrepant enough Stable features are the key to improving the OOD generalization,while the spurious correlation in data makes the model to lear