MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks

16 April 2021

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

6 / 6 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 68 2 0 14 Feb 2025
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 24 2 0 19 Dec 2023
Robust Stochastic Graph Generator for Counterfactual Explanations Mario Alfonso Prado-Romero Bardh Prenkaj Giovanni Stilo CML 6 3 0 18 Dec 2023
CLEAR: Generative Counterfactual Explanations on Graphs Jing Ma Ruocheng Guo Saumitra Mishra Aidong Zhang Jundong Li CML OOD 17 52 0 16 Oct 2022
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 162 1,766 0 02 Mar 2017