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Volume 9 Issue 2
Feb.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(2): 205-234
X. Liu, M. Y. Yan, L. Deng, G. Q. Li, X. C. Ye, and D. R. Fan, “Sampling methods for efficient training of graph convolutional networks: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 205–234, Feb. 2022. doi: 10.1109/JAS.2021.1004311
Citation: X. Liu, M. Y. Yan, L. Deng, G. Q. Li, X. C. Ye, and D. R. Fan, “Sampling methods for efficient training of graph convolutional networks: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 205–234, Feb. 2022. doi: 10.1109/JAS.2021.1004311
shu

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

doi: 10.1109/JAS.2021.1004311
  • 1.

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100086, China

  • 2.

    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing 100084, China

Funds:  This work was partially supported by the National Natural Science Foundation of China (61732018, 61872335, 61802367, 61876215), the Strategic Priority Research Program of Chinese Academy of Sciences (XDC05000000), Beijing Academy of Artificial Intelligence (BAAI), the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (2019A07), the Open Project of Zhejiang Laboratory, and a grant from the Institute for Guo Qiang, Tsinghua University
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    Abstract
  • 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.

     

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    • We provide the first comprehensive taxonomy of sampling methods for efficient GCN training
    • We compare sampling methods from multiple aspects and highlight their characteristics
    • We analyze multiple categories of sampling methods in theoretical and experimental manner

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