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Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years.
Depth-first search and linear graph algorithms
Robert Tarjan · 1972
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Emergence of scaling in random networks
Albert-László Barabási and Réka Albert · 1999
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Deep convolutional networks on graph-structured data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2015
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Semi-supervised classification with graph convolutional networks
Max Welling and Thomas N Kipf · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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Parameterized explainer for graph neural network
Degree: Decomposition based explanation for graph neural networks
Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, and Xia Hu · 2021
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[re] parameterized explainer for graph neural network
Lars Holdijk, Maarten Boon, Stijn Henckens, and Lysander de Jong · 2021
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A gentle introduction to graph neural networks
Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B Wiltschko · 2021
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Reinforcement learning enhanced explainer for graph neural networks
Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, and Dongsheng Li · 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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Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
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Interpreting graph neural networks for nlp with differentiable edge masking
Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov · 2020
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Higher-order explanations of graph neural networks via relevant walks
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Schütt, Klaus-Robert Müller, and Grégoire Montavon · 2020
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Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
Minh Vu and My T Thai · 2020
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Causal screening to interpret graph neural networks
Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, and Tat-seng Chua · 2020
Cited alongside, same era.
Xgnn: Towards model-level explanations of graph neural networks
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji · 2020
Cited alongside, same era.
Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 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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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
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Gflowcausal: Generative flow networks for causal discovery
Wenqian Li, Yinchuan Li, Shengyu Zhu, Yunfeng Shao, Jianye Hao, and Yan Pang · 2022
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Trajectory balance: Improved credit assignment in gflownets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2022
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Generative flow networks for discrete probabilistic modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, and Yoshua Bengio · 2022
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Cflownets: Continuous control with generative flow networks
Yinchuan Li, Shuang Luo, Haozhi Wang, and Jianye Hao · 2023
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