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Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process.
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Grad-cam: Visual explanations from deep networks via gradient-based localization
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Federico Baldassarre and Hossein Azizpour · 2019
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Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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Hodgenet: Graph neural networks for edge data
T Mitchell Roddenberry and Santiago Segarra · 2019
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Layerwise relevance visualization in convolutional text graph classifiers
Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt, and Leonhard Hennig · 2019
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Daniel Selsam and Nikolaj Bjørner · 2019
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Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L. Dill · 2019
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Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Graph transformer networks
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Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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Hyper-sagnn: a self-attention based graph neural network for hypergraphs
Ruochi Zhang, Yuesong Zou, and Jian Ma · 2019
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Spectral clustering with graph neural networks for graph pooling
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 2020
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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Dobrik Georgiev and Pietro Liò · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Johannes Gasteiger, Stefan Weißenberger, and Stephan Günnemann · 2022
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Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang · 2022
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Moldata, a molecular benchmark for disease and target based machine learning
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Privacy and transparency in graph machine learning: A unified perspective
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Pure transformers are powerful graph learners
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Improving semantic dependency parsing with higher-order information encoded by graph neural networks
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