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The problem of interpreting the decisions of machine learning is a well-researched and important.
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
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Protein function prediction via graph kernels
K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel · 2005
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Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad · 2008
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Modeling annotators: A generative approach to learning from annotator rationales
O. Zaidan and J. Eisner · 2008
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Graph-of-word and tw-idf: new approach to ad hoc ir
F. Rousseau and M. Vazirgiannis · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
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Rationalizing neural predictions
T. Lei, R. Barzilay, and T. Jaakkola · 2016
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" why should i trust you?" explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Müller · 2016
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Graph Attention Networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
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Eraser: A benchmark to evaluate rationalized nlp models
J. DeYoung, S. Jain, N. F. Rajani, E. Lehman, C. Xiong, R. Socher, and B. C. Wallace · 2019
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A benchmark for interpretability methods in deep neural networks
S. Hooker, D. Erhan, P.-J. Kindermans, and B. Kim · 2019
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A comparative study for unsupervised network representation learning
M. Khosla, V. Setty, and A. Anand · 2019
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Model agnostic interpretability of rankers via intent modelling
J. Singh and A. Anand · 2020
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Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
M. N. Vu and M. T. Thai · 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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Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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When comparing to ground truth is wrong: On evaluating gnn explanation methods
L. Faber, A. K. Moghaddam, and R. Wattenhofer · 2021
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Zorro: Valid, sparse, and stable explanations in graph neural networks
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J. Klicpera, A. Bojchevski, and S. Günnemann · 2019
Cited alongside, same era.
An evaluation of the human-interpretability of explanation
I. Lage, E. Chen, J. He, M. Narayanan, B. Kim, S. Gershman, and F. Doshi-Velez · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks
P. E. Pope, S. Kolouri, M. Rostami, C. E. Martin, and H. Hoffmann · 2019
Cited alongside, same era.
Do human rationales improve machine explanations?
J. Strout, Y. Zhang, and R. J. Mooney · 2019
Cited alongside, same era.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
Cited alongside, same era.
Gnn explainer: A tool for post-hoc explanation of graph neural networks
R. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec · 2019
Cited alongside, same era.
Graphlime: Local interpretable model explanations for graph neural networks
Q. Huang, M. Yamada, Y. Tian, D. Singh, D. Yin, and Y. Chang · 2020
Cited alongside, same era.
T. Funke, M. Khosla, M. Rathee, and A. Anand · 2021
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Utilizing graph machine learning within drug discovery and development
T. Gaudelet, B. Day, A. R. Jamasb, J. Soman, C. Regep, G. Liu, J. B. R. Hayter, R. Vickers, C. Roberts, J. Tang, D. Roblin, T. L. Blundell, M. M. Bronstein, and J. P. Taylor-King · 2021
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Adversarial attacks and defenses on graphs
W. Jin, Y. Li, H. Xu, Y. Wang, S. Ji, C. Aggarwal, and J. Tang · 2021
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Synthetic benchmarks for scientific research in explainable machine learning
Y. Liu, S. Khandagale, C. White, and W. Neiswanger · 2021
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A weighted patient network-based framework for predicting chronic diseases using graph neural networks
H. Lu and S. Uddin · 2021
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Teach me to explain: A review of datasets for explainable nlp
S. Wiegreffe and A. Marasović · 2021
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On explainability of graph neural networks via subgraph explorations
H. Yuan, H. Yu, J. Wang, K. Li, and S. Ji · 2021
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Relex: A model-agnostic relational model explainer
Y. Zhang, D. Defazio, and A. Ramesh · 2021
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Mucomid: A multitask graph convolutional learning framework for mirna-disease association prediction
T. N. Dong, S. Mucke, and M. Khosla · 2022
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