  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 memory costs. Therefore,
motivated by an urgent need in terms of efficiency and scalability in training
GCN, sampling methods are proposed and achieve 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.
