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Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Xin Liu Mingyu Yan Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan

自动化学报（英文版）2022，Vol.9Issue(2)：205-234,30.

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Xin Liu ¹Mingyu Yan ²Lei Deng ³Guoqi Li ⁴Xiaochun Ye ⁴Dongrui Fan³

作者信息

1. State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100086
2. School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China
3. State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100086,China
4. Department of Precision Instrument,Center for Brain Inspired Computing Research,Tsinghua University,Beijing 100084,China
折叠

摘要

关键词

Efficient training/graph convolutional networks (GCNs)/graph neural networks (GNNs)/sampling method

Key words

Efficient training/graph convolutional networks (GCNs)/graph neural networks (GNNs)/sampling method

引用本文复制引用

Xin Liu,Mingyu Yan,Lei Deng,Guoqi Li,Xiaochun Ye,Dongrui Fan..Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey[J].自动化学报（英文版）,2022,9(2):205-234,30.

基金项目

This work was partially supported by the National Natural Science Foundation of China(61732018,61872335,61802367,61876215),the Strategic Priority Research Program of Chinese Academy of Sciences(XDC05000000),Beijing Academy of Artificial Intelligence(BAAI),the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing(2019A07),the Open Project of Zhejiang Laboratory,and a grant from the Institute for Guo Qiang,Tsinghua University. （61732018,61872335,61802367,61876215）

自动化学报（英文版）

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ISSN：2329-9266

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