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Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Cited alongside, same era.
Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
Cited alongside, same era.
Redundancy-free computation for graph neural networks
Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken · 2020
Cited alongside, same era.
Improving the accuracy, scalability, and performance of graph neural networks with roc
Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken · 2020
Later among the works it cites.
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
Later among the works it cites.
Characterizing and understanding GCNs on GPU
Mingyu Yan, Zhaodong Chen, Lei Deng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, and Yuan Xie · 2020
Later among the works it cites.
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