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

doi:10.1109/JAS.2021.1004311

	Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey
	Liu, Xin 1,2; Yan, Mingyu 1; Deng, Lei 3; Li, Guoqi 3; Ye, Xiaochun 1; Fan, Dongrui 1,2
	2022-02-01
发表期刊	IEEE-CAA JOURNAL OF AUTOMATICA SINICA
ISSN	2329-9266
卷号	9 期号:2 页码:205-234
摘要	Graph convolutional networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.
关键词	Efficient training graph convolutional networks (GCNs) graph neural networks (GNNs) sampling method
DOI	10.1109/JAS.2021.1004311
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61732018] ; National Natural Science Foundation of China[61872335] ; National Natural Science Foundation of China[61802367] ; National Natural Science Foundation of China[61876215] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDC05000000] ; Beijing Academy of Artificial Intelligence (BAAI) ; Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing[2019A07] ; Open Project of Zhejiang Laboratory ; Institute for Guo Qiang, Tsinghua University
WOS研究方向	Automation & Control Systems
WOS类目	Automation & Control Systems
WOS记录号	WOS:000714714500005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：33[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17907
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yan, Mingyu
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100086, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Precis Instrument, Beijing 100084, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xin,Yan, Mingyu,Deng, Lei,et al. Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey[J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA,2022,9(2):205-234.
APA	Liu, Xin,Yan, Mingyu,Deng, Lei,Li, Guoqi,Ye, Xiaochun,&Fan, Dongrui.(2022).Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey.IEEE-CAA JOURNAL OF AUTOMATICA SINICA,9(2),205-234.
MLA	Liu, Xin,et al."Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey".IEEE-CAA JOURNAL OF AUTOMATICA SINICA 9.2(2022):205-234.