A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges

21 October 2022

Papers citing "A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges"

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Title
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 16 2 0 19 Dec 2023
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 99 87 0 05 Feb 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 225 485 0 31 Dec 2020
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 156 463 0 31 Dec 2020
Multi-scale Attributed Node Embedding Benedek Rozemberczki Carl Allen Rik Sarkar GNN 139 828 0 28 Sep 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 283 4,143 0 23 Aug 2019
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 152 1,748 0 02 Mar 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 231 1,801 0 25 Nov 2016