MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks

16 April 2021

Papers citing "MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks"

8 / 8 papers shown

Title
Recent Advances in Malware Detection: Graph Learning and Explainability Hossein Shokouhinejad Roozbeh Razavi-Far Hesamodin Mohammadian Mahdi Rabbani Samuel Ansong Griffin Higgins Ali Ghorbani AAML 76 2 0 14 Feb 2025
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 49 2 0 19 Dec 2023
Robust Stochastic Graph Generator for Counterfactual Explanations Mario Alfonso Prado-Romero Bardh Prenkaj Giovanni Stilo CML 13 3 0 18 Dec 2023
CLEAR: Generative Counterfactual Explanations on Graphs Jing Ma Ruocheng Guo Saumitra Mishra Aidong Zhang Jundong Li CML OOD 35 53 0 16 Oct 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 37 25 0 20 May 2022
Robust Counterfactual Explanations on Graph Neural Networks Mohit Bajaj Lingyang Chu Zihui Xue J. Pei Lanjun Wang P. C. Lam Yong Zhang OOD 40 96 0 08 Jul 2021
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction Jiahua Rao Shuangjia Zheng Yuedong Yang 21 46 0 01 Jul 2021
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 181 1,778 0 02 Mar 2017