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Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks.
Automating the construction of internet portals with machine learning
Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore · 2000
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Algorithmic Learning in a Random World
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Efficient k-nearest neighbor graph construction for generic similarity measures
Wei Dong, Moses Charikar, and Kai Li · 2011
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Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and U Edu · 2012
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Image-based recommendations on styles and substitutes
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel · 2015
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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The extreme classification repository: Multi-label datasets and code, 2016
K. Bhatia, K. Dahiya, H. Jain, P. Kar, A. Mittal, Y. Prabhu, and M. Varma · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Fast graph representation learning with pytorch geometric
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Towards reliable learning for high stakes applications
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Graph neural network for traffic forecasting: A survey
Weiwei Jiang and Jiayun Luo · 2022
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Graph representation learning in biomedicine and healthcare
Michelle M Li, Kexin Huang, and Marinka Zitnik · 2022
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Simplifying approach to node classification in graph neural networks
Sunil Kumar Maurya, Xin Liu, and Tsuyoshi Murata · 2022
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Conformal prediction beyond exchangeability
Rina Foygel Barber, Emmanuel J Candes, Aaditya Ramdas, and Ryan J Tibshirani · 2023
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Distribution free prediction sets for node classification
Jase Clarkson · 2023
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Improving fraud detection via hierarchical attention-based graph neural network
Yajing Liu, Zhengya Sun, and Wensheng Zhang · 2023
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Classification with valid and adaptive coverage
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Microsoft academic graph: When experts are not enough
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia · 2020
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Node classification with bounded error rates
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Uncertainty sets for image classifiers using conformal prediction
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Conformal inductive graph neural networks
Soroush H Zargarbashi and Aleksandar Bojchevski · 2023
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Conformal prediction sets for graph neural networks
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Similarity-navigated graph neural networks for node classification
Minhao Zou, Zhongxue Gan, Ruizhi Cao, Chun Guan, and Siyang Leng · 2023
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Spatial-aware conformal prediction for trustworthy hyperspectral image classification
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