CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks

5 February 2021

Ana Lucic

Maartje ter Hoeve

Gabriele Tolomei

Maarten de Rijke

Fabrizio Silvestri

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Papers citing "CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks"

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Title
Generating Skyline Explanations for Graph Neural Networks Dazhuo Qiu Haolai Che Arijit Khan Yinghui Wu 38 0 0 12 May 2025
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse Gregoire Fournier Sourav Medya BDL 44 0 0 11 May 2025
Robustness questions the interpretability of graph neural networks: what to do? Kirill Lukyanov Georgii Sazonov Serafim Boyarsky Ilya Makarov AAML 146 0 0 05 May 2025
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning Volkan Bakir Polat Goktas Sureyya Akyuz 52 0 0 26 Apr 2025
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks Emré Anakok Pierre Barbillon Colin Fontaine Elisa Thébault 47 0 0 19 Mar 2025
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 73 2 0 14 Feb 2025
Natural Language Counterfactual Explanations for Graphs Using Large Language Models Flavio Giorgi Cesare Campagnano Fabrizio Silvestri Gabriele Tolomei LRM 41 1 0 28 Jan 2025
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach Ruichu Cai Yuxuan Zhu Xuexin Chen Yuan Fang Min-man Wu Jie Qiao Z. Hao 51 7 0 31 Dec 2024
GraphXAIN: Narratives to Explain Graph Neural Networks Mateusz Cedro David Martens 47 0 0 04 Nov 2024
Explainable Graph Neural Networks Under Fire Zhong Li Simon Geisler Yuhang Wang Stephan Günnemann M. Leeuwen AAML 43 0 0 10 Jun 2024
Explaining Expert Search and Team Formation Systems with ExES Kiarash Golzadeh Lukasz Golab Jaroslaw Szlichta 27 0 0 21 May 2024
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation Xuexin Chen Ruichu Cai Zhengting Huang Yuxuan Zhu Julien Horwood Zhifeng Hao Zijian Li Jose Miguel Hernandez-Lobato AAML 36 2 0 13 Feb 2024
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 21 0 0 07 Feb 2024
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 44 2 0 19 Dec 2023
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion Jialin Chen Shirley Wu Abhijit Gupta Rex Ying DiffM 39 4 0 30 Oct 2023
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? Yongqiang Chen Yatao Bian Kaiwen Zhou Binghui Xie Bo Han James Cheng OOD 28 34 0 29 Oct 2023
DEGREE: Decomposition Based Explanation For Graph Neural Networks Qizhang Feng Ninghao Liu Fan Yang Ruixiang Tang Mengnan Du Xia Hu 23 22 0 22 May 2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability Indro Spinelli Michele Guerra F. Bianchi Simone Scardapane 36 0 0 14 Apr 2023
Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis Haoyu He Yuede Ji H. H. Huang 23 20 0 26 Mar 2023
GANExplainer: GAN-based Graph Neural Networks Explainer Yiqiao Li Jianlong Zhou Boyuan Zheng Fang Chen LLMAG 32 4 0 30 Dec 2022
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning Jiasheng Si Yingjie Zhu Deyu Zhou 27 12 0 02 Dec 2022
Global Counterfactual Explainer for Graph Neural Networks Mert Kosan Zexi Huang Sourav Medya Sayan Ranu Ambuj K. Singh 26 47 0 21 Oct 2022
CLEAR: Generative Counterfactual Explanations on Graphs Jing Ma Ruocheng Guo Saumitra Mishra Aidong Zhang Jundong Li CML OOD 30 53 0 16 Oct 2022
L2XGNN: Learning to Explain Graph Neural Networks G. Serra Mathias Niepert 33 7 0 28 Sep 2022
Evaluating Explainability for Graph Neural Networks Chirag Agarwal Owen Queen Himabindu Lakkaraju Marinka Zitnik 40 99 0 19 Aug 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 30 25 0 20 May 2022
Cardinality-Minimal Explanations for Monotonic Neural Networks Ouns El Harzli Bernardo Cuenca Grau Ian Horrocks FAtt 35 5 0 19 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 45 104 0 16 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 37 96 0 08 Jul 2021
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods Chirag Agarwal Marinka Zitnik Himabindu Lakkaraju 19 51 0 16 Jun 2021
Counterfactual Explanations for Neural Recommenders Khanh Tran Azin Ghazimatin Rishiraj Saha Roy AAML CML 52 65 0 11 May 2021
Few-Shot Graph Learning for Molecular Property Prediction Zhichun Guo Chuxu Zhang W. Yu John E. Herr Olaf Wiest Meng-Long Jiang Nitesh V. Chawla AI4CE 116 169 0 16 Feb 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 167 592 0 31 Dec 2020
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification Rui Yang Wenrui Dai Chenglin Li Junni Zou H. Xiong 25 20 0 07 Dec 2020
BRPO: Batch Residual Policy Optimization Kentaro Kanamori Yinlam Chow Takuya Takagi Hiroki Arimura Honglak Lee Ken Kobayashi Craig Boutilier OffRL 139 46 0 08 Feb 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017