Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction

1 July 2021

Papers citing "Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction"

9 / 9 papers shown

Title
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks Jiahua Rao Jiancong Xie Hanjing Lin Shuangjia Zheng Zhen Wang Yuedong Yang 19 0 0 07 Feb 2024
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey R. Alizadehsani Solomon Sunday Oyelere Sadiq Hussain Rene Ripardo Calixto V. H. C. de Albuquerque M. Roshanzamir Mohamed Rahouti Senthil Kumar Jagatheesaperumal 34 14 0 21 Sep 2023
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability Zhiqiang Zhong A. Barkova Davide Mottin 14 8 0 16 Feb 2023
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment Tianxiang Zhao Dongsheng Luo Xiang Zhang Suhang Wang 18 7 0 07 Jan 2023
Towards Explainable Motion Prediction using Heterogeneous Graph Representations Sandra Carrasco Limeros Sylwia Majchrowska Joakim Johnander Christoffer Petersson David Fernández Llorca 21 15 0 07 Dec 2022
Towards Faithful and Consistent Explanations for Graph Neural Networks Tianxiang Zhao Dongsheng Luo Xiang Zhang Suhang Wang FAtt 42 18 0 27 May 2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection Bingzhe Wu Jintang Li Junchi Yu Yatao Bian Hengtong Zhang ... Guangyu Sun Peng Cui Zibin Zheng Zhe Liu P. Zhao OOD 23 25 0 20 May 2022
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 162 589 0 31 Dec 2020
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 219 1,329 0 12 Feb 2018