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The opaque reasoning of Graph Neural Networks induces a lack of human trust.
Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Zou, and Been Kim · 1902
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The magical number seven, plus or minus two: Some limits on our capacity for processing information
George Armitage Miller · 1956
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Cluster analysis of multivariate data: efficiency versus interpretability of classifications
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Classification and regression trees
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Emergence of scaling in random networks
Albert-László Barabási and Réka Albert · 1999
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Now you see me (cme): concept-based model extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò, and Adrian Weller · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Meme: generating rnn model explanations via model extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, and Pietro Liò · 2012
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Gdpr. general data protection regulation., 2017
EUGDPR · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S Jaakkola · 2018
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
Minh Vu and My T Thai · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
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Gnnexplainer: Generating explanations for graph neural networks
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The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Who is afraid of black box algorithms? on the epistemological and ethical basis of trust in medical ai
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Gcexplainer: Human-in-the-loop concept-based explanations for graph neural networks
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Robust counterfactual explanations on graph neural networks
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Entropy-based logic explanations of neural networks
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Evaluating the quality of machine learning explanations: A survey on methods and metrics
Jianlong Zhou, Amir H Gandomi, Fang Chen, and Andreas Holzinger · 2021
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Max W Shen · 2022
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Cf-gnnexplainer: Counterfactual explanations for graph neural networks
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