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With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network.
Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
A. Binder, G. Montavon, S. Lapuschkin, K.-R. Müller, and W. Samek · 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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Rate–distortion theory and human perception
C. R. Sims · 2016
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Revisiting semi-supervised learning with graph embeddings
Z. Yang, W. W. Cohen, and R. Salakhutdinov · 2016
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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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Learning important features through propagating activation differences
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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Learning to explain: An information-theoretic perspective on model interpretation
J. Chen, L. Song, M. J. Wainwright, and M. I. Jordan · 2018
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Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
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Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
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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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Invase: Instance-wise variable selection using neural networks
J. Yoon, J. Jordon, and M. van der Schaar · 2018
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Do transformer attention heads provide transparency in abstractive summarization?
J. Baan, M. ter Hoeve, M. van der Wees, A. Schuth, and M. de Rijke · 2019
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What made you do this? understanding black-box decisions with sufficient input subsets
B. Carter, J. Mueller, S. Jain, and D. Gifford · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
S. Hooker, D. Erhan, P.-J. Kindermans, and B. Kim · 2019
Cited alongside, same era.
Finding interpretable concept spaces in node embeddings using knowledge bases
M. Idahl, M. Khosla, and A. Anand · 2019
Cited alongside, same era.
Explaine: An approach for explaining network embedding-based link predictions
B. Kang, J. Lijffijt, and T. De Bie · 2019
Cited alongside, same era.
A comparative study for unsupervised network representation learning
Graphlime: Local interpretable model explanations for graph neural networks
Q. Huang, M. Yamada, Y. Tian, D. Singh, D. Yin, and Y. Chang · 2020
Later among the works it cites.
Aligning faithful interpretations with their social attribution
A. Jacovi and Y. Goldberg · 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
Later among the works it cites.
Evaluating attribution for graph neural networks
B. Sanchez-Lengeling, J. Wei, B. Lee, E. Reif, P. Wang, W. W. Qian, K. McCloskey, L. Colwell, and A. Wiltschko · 2020
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Interpreting graph neural networks for nlp with differentiable edge masking
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M. Khosla, V. Setty, and A. Anand · 2019
Cited alongside, same era.
The (un) reliability of saliency methods
P.-J. Kindermans, S. Hooker, J. Adebayo, M. Alber, K. T. Schütt, S. Dähne, D. Erhan, and B. Kim · 2019
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks
P. E. Pope, S. Kolouri, M. Rostami, et al · 2019
Cited alongside, same era.
Learning to deceive with attention-based explanations
D. Pruthi, M. Gupta, B. Dhingra, G. Neubig, and Z. C. Lipton · 2019
Cited alongside, same era.
A formal approach to explainability
L. Wolf, T. Galanti, and T. Hazan · 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.
M. S. Schlichtkrull, N. De Cao, and I. Titov · 2020
Later among the works it cites.
Higher-order explanations of graph neural networks via relevant walks
T. Schnake, O. Eberle, J. Lederer, S. Nakajima, K. T. Schütt, K.-R. Müller, and G. Montavon · 2020
Later among the works it cites.
Valid explanations for learning to rank models
J. Singh, Z. Wang, M. Khosla, and A. Anand · 2020
Later among the works it cites.
Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
M. N. Vu and M. T. Thai · 2020
Later among the works it cites.
Xgnn: Towards model-level explanations of graph neural networks
H. Yuan, J. Tang, X. Hu, and S. Ji · 2020
Later among the works it cites.
Explainability in graph neural networks: A taxonomic survey
H. Yuan, H. Yu, S. Gui, and S. Ji · 2020
Later among the works it cites.
GraphSVX: Shapley value explanations for graph neural networks
A. Duval and F. D. Malliaros · 2021
Closest in time.
Gnes: Learning to explain graph neural networks
Y. Gao, T. Sun, R. Bhatt, D. Yu, S. Hong, and L. Zhao · 2021
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Edge-level explanations for graph neural networks by extending explainability methods for convolutional neural networks
T. Kasanishi, X. Wang, and T. Yamasaki · 2021
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Generative causal explanations for graph neural networks
W. Lin, H. Lan, and B. Li · 2021
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Extracting per query valid explanations for blackbox learning-to-rank models
J. Singh, M. Khosla, W. Zhenye, and A. Anand · 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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