CASIA OpenIR  > 学术期刊  > IEEE/CAA Journal of Automatica Sinica
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey
Xin Liu; Mingyu Yan; Lei Deng; Guoqi Li; Xiaochun Ye; Dongrui Fan
Source PublicationIEEE/CAA Journal of Automatica Sinica
ISSN2329-9266
2022
Volume9Issue:2Pages:205-234
AbstractGraph 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.
KeywordEfficient training graph convolutional networks (GCNs) graph neural networks (GNNs) sampling method
DOI10.1109/JAS.2021.1004311
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45985
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
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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