Tianrui Jia

Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization

Graph neural networks (GNNs) have been demonstrated to perform well in graph representation learning, but always lacking in generalization capability when tackling out-of-distribution (OOD) data. Graph invariant learning methods, backed by the invariance principle among defined multiple environments…