Robustness questions the interpretability of graph neural networks: what to do?

5 May 2025

Papers citing "Robustness questions the interpretability of graph neural networks: what to do?"

28 / 28 papers shown

Title
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs Haibin Zheng Haiyang Xiong Jinyin Chen Hao-Shang Ma Guohan Huang 104 31 0 25 Oct 2022
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis Xuanyuan Han Pietro Barbiero Dobrik Georgiev Lucie Charlotte Magister Pietro Lio MILM 72 41 0 22 Aug 2022
Conflicting Interactions Among Protection Mechanisms for Machine Learning Models S. Szyller Nadarajah Asokan AAML 81 7 0 05 Jul 2022
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability Enyan Dai Tianxiang Zhao Huaisheng Zhu Jun Xu Zhimeng Guo Hui Liu Jiliang Tang Suhang Wang 95 144 0 18 Apr 2022
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation Sixiao Zhang Hongxu Chen Xiangguo Sun Yicong Li Guandong Xu AAML SSL 85 43 0 20 Jan 2022
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 70 23 0 14 Feb 2021
On Explainability of Graph Neural Networks via Subgraph Explorations Hao Yuan Haiyang Yu Jie Wang Kang Li Shuiwang Ji FAtt 83 395 0 09 Feb 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 199 146 0 05 Feb 2021
Membership Inference Attack on Graph Neural Networks Iyiola E. Olatunji Wolfgang Nejdl Megha Khosla AAML 119 102 0 17 Jan 2021
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 161 175 0 20 Oct 2020
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking Michael Schlichtkrull Nicola De Cao Ivan Titov AI4CE 129 220 0 01 Oct 2020
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Xiang Zhang Marinka Zitnik AAML 100 297 0 15 Jun 2020
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 88 335 0 19 Jun 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 89 79 0 27 May 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 155 1,334 0 10 Mar 2019
Fast Graph Representation Learning with PyTorch Geometric Matthias Fey J. E. Lenssen 3DH GNN 3DPC 256 4,371 0 06 Mar 2019
Graph Neural Networks: A Review of Methods and Applications Jie Zhou Ganqu Cui Shengding Hu Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li Maosong Sun AI4CE GNN 1.1K 5,551 0 20 Dec 2018
Adversarial Attacks on Neural Networks for Graph Data Daniel Zügner Amir Akbarnejad Stephan Günnemann GNN AAML OOD 173 1,072 0 21 May 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 155 3,989 0 06 Feb 2018
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten AAML 141 1,407 0 31 Oct 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 261 4,287 0 22 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 321 12,151 0 19 Jun 2017
MagNet: a Two-Pronged Defense against Adversarial Examples Dongyu Meng Hao Chen AAML 56 1,209 0 25 May 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 420 3,824 0 28 Feb 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,092 0 16 Feb 2016
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 120 3,078 0 14 Nov 2015
Image-based Recommendations on Styles and Substitutes Julian McAuley C. Targett Javen Qinfeng Shi Anton Van Den Hengel 132 2,415 0 15 Jun 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,145 0 20 Dec 2014