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

文献类型：期刊论文

作者Liu, Xin2,3; Yan, Mingyu3; Deng, Lei1; Li, Guoqi1; Ye, Xiaochun3; Fan, Dongrui2,3
刊名IEEE-CAA JOURNAL OF AUTOMATICA SINICA
出版日期2022-02-01
卷号9期号:2页码:205-234
关键词Efficient training graph convolutional networks (GCNs) graph neural networks (GNNs) sampling method
ISSN号2329-9266
DOI10.1109/JAS.2021.1004311
英文摘要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.
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
语种英语
WOS记录号WOS:000714714500005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17906]  
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.Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Precis Instrument, Beijing 100084, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100086, 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.

入库方式： OAI收割

来源：计算技术研究所

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