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Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.
Information theory and statistics
Solomon Kullback · 1997
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Causality: Models, Reasoning, and Inference
Judea Pearl · 2000
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A characterization of interventional distributions in semi-markovian causal models
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Visualizing high-dimensional data using t-sne
G.E. van der Maaten, L.J.P.; Hinton · 2008
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts · 2013
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A three-way decomposition of a total effect into direct, indirect, and interactive effects
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Adam: A method for stochastic optimization
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Causal inference in statistics: A primer
Judea Pearl, Madelyn Glymour, and Nicholas P Jewell · 2016
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Moleculenet: A benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay S. Pande · 2017
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Invariance, causality and robustness
Peter Bühlmann · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Graph neural networks with convolutional ARMA filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, and Cesare Alippi · 2019
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Rubi: Reducing unimodal biases for visual question answering
Rémi Cadène, Corentin Dancette, Hedi Ben-younes, Matthieu Cord, and Devi Parikh · 2019
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On the equivalence between graph isomorphism testing and function approximation with gnns
Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Distance encoding: Design provably more powerful neural networks for graph representation learning
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec · 2020
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Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
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ASAP: adaptive structure aware pooling for learning hierarchical graph representations
Ekagra Ranjan, Soumya Sanyal, and Partha P. Talukdar · 2020
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Improved protein structure prediction using potentials from deep learning
Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Zídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, and Demis Hassabis · 2020
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Unshuffling data for improved generalization
Damien Teney, Ehsan Abbasnejad, and Anton van den Hengel · 2020
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Boris Knyazev, Graham W. Taylor, and Mohamed R. Amer · 2019
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Self-attention graph pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang · 2019
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How powerful are graph neural networks?
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Gnnexplainer: Generating explanations for graph neural networks
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Risk variance penalization: From distributional robustness to causality
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2020
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Empirical or invariant risk minimization? A sample complexity perspective
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R. Varshney · 2021
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Size-invariant graph representations for graph classification extrapolations
Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro · 2021
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Redunet: A white-box deep network from the principle of maximizing rate reduction
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Environment inference for invariant learning
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Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Does invariant risk minimization capture invariance?
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, and Nathan Srebro · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, and Aaron C. Courville · 2021
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The risks of invariant risk minimization
Elan Rosenfeld, Pradeep Kumar Ravikumar, and Andrej Risteski · 2021
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On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
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