103 citations · 332 across the 10 of their papers we have counts for
7 papers · 1 filter
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,…
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…
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…
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…
Explainability in Graph Neural Networks: A Taxonomic Survey
Hao Yuan, Haiyang Yu, Shurui Gui +1
Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpreta…
Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions
Zhengyang Wang, Hao Yuan, Shuiwang Ji
The key idea of variational auto-encoders (VAEs) resembles that of traditional auto-encoder models in which spatial information is supposed to be explicitly encoded in the latent s…
Pixel Deconvolutional Networks
Hongyang Gao, Hao Yuan, Zhengyang Wang +1
Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models fo…