94 citations · 214 across the 15 of their papers we have counts for
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cs.LG2022★ 2 cited
FlowX: Towards Explainable Graph Neural Networks via Message Flows
Shurui Gui, Hao Yuan, Jie Wang +3
We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. While most current methods focus on explaining graph nodes,…
cs.LG2022★ 30 cited
GOOD: A Graph Out-of-Distribution Benchmark
Shurui Gui, Xiner Li, Limei Wang +1
Out-of-distribution (OOD) learning deals with scenarios in which training and test data follow different distributions. Although general OOD problems have been intensively studied…