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

doi:10.1109/JAS.2021.1004311

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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
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2022
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
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45985
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Xin Liu,Mingyu Yan,Lei Deng,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	Xin Liu,Mingyu Yan,Lei Deng,Guoqi Li,Xiaochun Ye,&Dongrui Fan.(2022).Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey.IEEE/CAA Journal of Automatica Sinica,9(2),205-234.
MLA	Xin Liu,et al."Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey".IEEE/CAA Journal of Automatica Sinica 9.2(2022):205-234.

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