Good: A graph out-of-distribution benchmark

S Gui, X Li, L Wang, S Ji - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Abstract Out-of-distribution (OOD) learning deals with scenarios in which training and test
data follow different distributions. Although general OOD problems have been intensively …

Joint learning of label and environment causal independence for graph out-of-distribution generalization

S Gui, M Liu, X Li, Y Luo, S Ji - Advances in Neural …, 2023 - proceedings.neurips.cc
We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD
algorithms either rely on restricted assumptions or fail to exploit environment information in …

Collaboration-aware graph convolutional network for recommender systems

Y Wang, Y Zhao, Y Zhang, T Derr - … of the ACM web conference 2023, 2023 - dl.acm.org
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems
by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless …