Fetching the paper…
Reading the bibliography…
Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
A.K. Debnath, R.L. Lopez de Compadre, G. Debnath, A.J. Shusterman, and C. Hansch · 1991
Earlier work this paper cites.
Regulation (EU) 2016/679 of the European Parliament (GDPR)
EU · 2016
Earlier work this paper cites.
Why should I trust you? Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Training sparse neural networks
Suraj Srinivas, Akshayvarun Subramanya, and R. Venkatesh Babu · 2017
Earlier work this paper cites.
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas · 2017
Earlier work this paper cites.
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
Earlier work this paper cites.
Considerations for Evaluation and Generalization in Interpretable Machine Learning
Finale Doshi-Velez and Been Kim · 2018
Earlier work this paper cites.
Explainable AI: The new 42?
R. Goebel, A. Chander, K. Holzinger, F. Lecue, Z. Akata, S. Stumpf, P. Kieseberg, and A. Holzinger · 2018
Earlier work this paper cites.
Inverse Classification for Comparison-based Interpretability in Machine Learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2018
Earlier work this paper cites.
Adversarial attack and defense on graph data: A survey
Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, and Bo Li · 2018
Earlier work this paper cites.
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
Earlier work this paper cites.
Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 2018
Earlier work this paper cites.
Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour · 2019
Earlier work this paper cites.
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, and Jian Tang · 2019
Earlier work this paper cites.
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Bo Kang, Jefrey Lijffijt, and Tijl De Bie · 2019
Earlier work this paper cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
Cited alongside, same era.
Interpretable Machine Learning
Christoph Molnar · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks
Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, and Heiko Hoffmann · 2019
Cited alongside, same era.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller · 2019
Cited alongside, same era.
Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Cited alongside, same era.
GNNExplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Cited alongside, same era.
Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
Minh N. Vu and My T. Thai · 2020
Later among the works it cites.
A compact review of molecular property prediction with graph neural networks
Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer · 2020
Later among the works it cites.
Benchmarking and survey of explanation methods for black box models
Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, and Salvatore Rinzivillo · 2021
Closest in time.
Machine learning on graphs: A model and comprehensive taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, and Kevin Murphy · 2021
Closest in time.
Graphsvx: Shapley value explanations for graph neural networks
Alexandre Duval and Fragkiskos D. Malliaros · 2021
Closest in time.
Operationalizing Human-Centered Perspectives in Explainable AI
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
Solon Barocas, Andrew D. Selbst, and Manish Raghavan · 2020
Cited alongside, same era.
Contrastive Graph Neural Network Explanation
Lukas Faber, Amin K Moghaddam, and Roger Wattenhofer · 2020
Cited alongside, same era.
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang · 2020
Cited alongside, same era.
DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, and Hiroki Arimura · 2020
Cited alongside, same era.
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Ana Lucic, Hinda Haned, and Maarten de Rijke · 2020
Cited alongside, same era.
Parameterized Explainer for Graph Neural Network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
Cited alongside, same era.
Upol Ehsan, Q Vera Liao, and et al · 2021
Closest in time.
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
Timo Freiesleben · 2021
Closest in time.
Few-shot graph learning for molecular property prediction
Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, and Nitesh V. Chawla · 2021
Closest in time.
The use and misuse of counterfactuals in ethical machine learning
Atoosa Kasirzadeh and Andrew Smart · 2021
Closest in time.
Generative Causal Explanations for Graph Neural Networks
Wanyu Lin, Hao Lan, and Baochun Li · 2021
Closest in time.
Interpreting graph neural networks for NLP with differentiable edge masking
Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov · 2021
Closest in time.
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Lisa Schut, Oscar Key, and Rory McGrath · 2021
Closest in time.
A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
Ilia Stepin, Jose M Alonso, Alejandro Catala, and Martín Pereira-Fariña · 2021
Closest in time.
Mars: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
Closest in time.
On Explainability of Graph Neural Networks via Subgraph Explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
Closest in time.
Focus: Flexible optimizable counterfactual explanations for tree ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned, and Maarten de Rijke · 2022
Closest in time.