115 citations · 307 across the 9 of their papers we have counts for
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cs.LG2021
ProtGNN: Towards Self-Explaining Graph Neural Networks
Zaixi Zhang, Qi Liu, Hao Wang +2
Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions made by GNNs. Existing explanation methods mainly focus on post-hoc e…
cs.LG2021★ 3 cited
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang +4
As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxil…
cs.LG2020★ 115 cited
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Zhongkai Hao, Chengqiang Lu, Zheyuan Hu +5
Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of sc…