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Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition.
Adversarial attacks on graph neural networks via meta learning
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An analysis of the greedy algorithm for the submodular set covering problem
L. A. Wolsey · 1982
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
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Derivation and validation of toxicophores for mutagenicity prediction
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Comparison of descriptor spaces for chemical compound retrieval and classification
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Graphs in molecular biology
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Friendship and mobility: user movement in location-based social networks
E. Cho, S. A. Myers, and J. Leskovec · 2011
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Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
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Exact epidemic models on graphs using graph-automorphism driven lumping
P. L. Simon, M. Taylor, and I. Z. Kiss · 2011
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Maxout networks
I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Submodular optimization with submodular cover and submodular knapsack constraints
R. K. Iyer and J. A. Bilmes · 2013
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On the number of linear regions of deep neural networks
G. Montúfar, R. Pascanu, K. Cho, and Y. Bengio · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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pkcsm: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures
D. E. Pires, T. L. Blundell, and D. B. Ascher · 2015
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M. Denil, S. G. Colmenarejo, S. Cabi, D. Saxton, and N. de Freitas · 2017
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Y. Duan, M. Andrychowicz, B. C. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Counterfactual explanations of machine learning predictions: opportunities and challenges for ai safety
K. Sokol and P. A. Flach · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
K. Xu, H. Chen, S. Liu, P.-Y. Chen, T.-W. Weng, M. Hong, and X. Lin · 2019
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A deeper graph neural network for recommender systems
R. Yin, K. Li, G. Zhang, and J. Lu · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec · 2019
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T. Freiesleben · 2020
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Exact and consistent interpretation for piecewise linear neural networks: A closed form solution
L. Chu, X. Hu, J. Hu, L. Wang, and J. Pei · 2018
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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Top-down neural attention by excitation backprop
J. Zhang, S. A. Bargal, Z. Lin, J. Brandt, X. Shen, and S. Sclaroff · 2018
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Link prediction based on graph neural networks
M. Zhang and Y. Chen · 2018
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Adversarial attacks on neural networks for graph data
D. Zügner, A. Akbarnejad, and S. Günnemann · 2018
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Certifiable robustness to graph perturbations
A. Bojchevski and S. Günnemann · 2019
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Submodular cost submodular cover with an approximate oracle
V. Crawford, A. Kuhnle, and M. Thai · 2019
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Drug–target affinity prediction using graph neural network and contact maps
M. Jiang, Z. Li, S. Zhang, S. Wang, X. Wang, Q. Yuan, and Z. Wei · 2020
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Parameterized explainer for graph neural network
D. Luo, W. Cheng, D. Xu, W. Yu, B. Zong, H. Chen, and X. Zhang · 2020
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Causal interpretability for machine learning-problems, methods and evaluation
R. Moraffah, M. Karami, R. Guo, A. Raglin, and H. Liu · 2020
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Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
M. Vu and M. T. Thai · 2020
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Adversarial attacks and defenses in images, graphs and text: A review
H. Xu, Y. Ma, H.-C. Liu, D. Deb, H. Liu, J.-L. Tang, and A. K. Jain · 2020
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Xgnn: Towards model-level explanations of graph neural networks
H. Yuan, J. Tang, X. Hu, and S. Ji · 2020
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[re] parameterized explainer for graph neural network
L. Holdijk, M. Boon, S. Henckens, and L. de Jong · 2021
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DIG: A turnkey library for diving into graph deep learning research
M. Liu, Y. Luo, L. Wang, Y. Xie, H. Yuan, S. Gui, H. Yu, Z. Xu, J. Zhang, Y. Liu, K. Yan, H. Liu, C. Fu, B. Oztekin, X. Zhang, and S. Ji · 2021
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Cf-gnnexplainer: Counterfactual explanations for graph neural networks
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Meg: Generating molecular counterfactual explanations for deep graph networks
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Graph neural networks for automated de novo drug design
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On explainability of graph neural networks via subgraph explorations
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