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Prior works on formalizing explanations of a graph neural network (GNN) focus on a single use case - to preserve the prediction results through identifying important edges and nodes.
Graph drawing by force-directed placement
Thomas MJ Fruchterman and Edward M Reingold · 1991
Earlier work this paper cites.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
Christian Blum and Andrea Roli · 2003
Earlier work this paper cites.
Data analysis in public social networks
L. Takac · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Docuviz: visualizing collaborative writing
Dakuo Wang, Judith S Olson, Jingwen Zhang, Trung Nguyen, and Gary M Olson · 2015
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, E. Price, and Nathan Srebro · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
Earlier work this paper cites.
Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Earlier work this paper cites.
A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
Earlier work this paper cites.
Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
Multi-label image recognition with graph convolutional networks
Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, and Y. Guo · 2019
Later among the works it cites.
Understanding deep networks via extremal perturbations and smooth masks
Ruth Fong, Mandela Patrick, and Andrea Vedaldi · 2019
Later among the works it cites.
signSGD via zeroth-order oracle
Sijia Liu, Pin-Yu Chen, Xiangyi Chen, and Mingyi Hong · 2019
Later among the works it cites.
Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
Later among the works it cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Later among the works it cites.
Topology attack and defense for graph neural networks: An optimization perspective
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Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
Cited alongside, same era.
Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
Cited alongside, same era.
Explaining explanations: An overview of interpretability of machine learning
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
Cited alongside, same era.
Deep face recognition: A survey
Iacopo Masi, Yue Wu, Tal Hassner, and Prem Natarajan · 2018
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
Cited alongside, same era.
Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Visual interpretability for deep learning: a survey
Quan-shi Zhang and Song-Chun Zhu · 2018
Cited alongside, same era.
Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
Later among the works it cites.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.
Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Later among the works it cites.
Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
Later among the works it cites.
Understanding the role of individual units in a deep neural network
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba · 2020
Later among the works it cites.
Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
Enyan Dai and Suhang Wang · 2020
Later among the works it cites.
A survey of deep learning techniques for autonomous driving
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, and Gigel Macesanu · 2020
Later among the works it cites.
Autoaiviz: opening the blackbox of automated artificial intelligence with conditional parallel coordinates
Daniel Karl I Weidele, Justin D Weisz, Erick Oduor, Michael Muller, Josh Andres, Alexander Gray, and Dakuo Wang · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Later among the works it cites.
Explainable deep learning: A field guide for the uninitiated
Ning Xie, Gabrielle Ras, Marcel van Gerven, and Derek Doran · 2020
Later among the works it cites.