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Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns.
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László Lovász · 2012
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Thomas N. Kipf and Max Welling · 2017
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Évariste Daller, Sébastien Bougleux, Luc Brun, and Olivier Lézoray · 2018
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Andreas Loukas and Pierre Vandergheynst · 2018
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Graphon games: A statistical framework for network games and interventions
Francesca Parise and Asuman Ozdaglar · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Graph attention networks
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Dataset distillation
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec · 2018
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Muhan Zhang and Yixin Chen · 2018
Dataset distillation with infinitely wide convolutional networks
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Luana Ruiz, Luiz FO Chamon, and Alejandro Ribeiro · 2021
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Discrete graph structure learning for forecasting multiple time series
Chao Shang, Jie Chen, and Jinbo Bi · 2021
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Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Yuanxing Ning, Philip S Yu, and Lifang He · 2021
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Hongteng Xu, Dixin Luo, Lawrence Carin, and Hongyuan Zha · 2021
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Fast graph representation learning with pytorch geometric, Mar 2019
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Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He · 2019
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Graphon control of large-scale networks of linear systems
Shuang Gao and Peter E Caines · 2019
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Surge: a fast open-source chemical graph generator
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Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing
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