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Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph.
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Towards A Rigorous Science of Interpretable Machine Learning
F. Doshi-Velez and B. Kim · 2017
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Inductive representation learning on large graphs
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Interpretable & Explorable Approximations of Black Box Models, 2017
H. Lakkaraju, E. Kamar, R. Caruana, and J. Leskovec · 2017
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A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Axiomatic Attribution for Deep Networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
L. Zintgraf, T. Cohen, T. Adel, and M. Welling · 2017
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
A. Adadi and M. Berrada · 2018
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Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
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C. Yeh, J. Kim, I. Yen, and P. Ravikumar · 2018
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Graph convolutional neural networks for web-scale recommender systems
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Sanity checks for saliency maps
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Relational inductive biases, deep learning, and graph networks
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Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
Jianbo Chen, Le Song, Martin J Wainwright, and Michael I Jordan · 2018
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance
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Hierarchical graph representation learning with differentiable pooling
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec · 2018
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Link prediction based on graph neural networks
M. Zhang and Y. Chen · 2018
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Deep Learning on Graphs: A Survey
Z. Zhang, Peng C., and W. Zhu · 2018
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Graph Neural Networks: A Review of Methods and Applications
J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, and M. Sun · 2018
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Modeling polypharmacy side effects with graph convolutional networks
M. Zitnik, M. Agrawal, and J. Leskovec · 2018
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Supervised community detection with line graph neural networks
Z. Chen, L. Li, and J. Bruna · 2019
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Explaine: An approach for explaining network embedding-based link predictions
Bo Kang, Jefrey Lijffijt, and Tijl De Bie · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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Position-aware graph neural networks
J. You, Rex Ying, and J. Leskovec · 2019
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