70 citations · 71 across the 3 of their papers we have counts for
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cs.LG2021
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
Hyeoncheol Cho, Youngrock Oh, Eunjoo Jeon
Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth…
cs.LG2018★ 70 cited
Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation
Hyeoncheol Cho, Insung S. Choi
We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph…