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

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...

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Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica 2022-02, Vol.9 (2), p.205-234
Main Authors: Liu, Xin, Yan, Mingyu, Deng, Lei, Li, Guoqi, Ye, Xiaochun, Fan, Dongrui
Format: Article
Language:English
Subjects:
Artificial neural networks
Computational modeling
Costs
Efficient training
graph convolutional networks (GCNs)
graph neural networks (GNNs)
Graph representations
Graphical representations
Neural networks
sampling method
Sampling methods
Scalability
Taxonomy
Training
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Summary: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.
ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2021.1004311