103 citations · 332 across the 10 of their papers we have counts for
Showing 2021Show all
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cs.LG2021★ 5 cited
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
cs.LG2021★ 90 cited
On Explainability of Graph Neural Networks via Subgraph Explorations
Hao Yuan, Haiyang Yu, Jie Wang +2
We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explainin…
cs.LG2021★ 5 cited
Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Hao Yuan, Shuiwang Ji
Investigating graph feature learning becomes essentially important with the emergence of graph data in many real-world applications. Several graph neural network approaches are pro…