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Good: A graph out-of-distribution benchmark
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 …
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 …
algorithms either rely on restricted assumptions or fail to exploit environment information in …
Collaboration-aware graph convolutional network for recommender systems
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems
by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless …
by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless …