Graph Convolutional Networks(GCNs) play a crucial role in graph learning
tasks, however, learning graph embedding with few supervised signals is still a
difficult problem. In this paper, we propose a novel training algorithm for
Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training
Algorithm, combined with self-supervised learning approach, focusing on
improving the generalization performance of GCNs on graphs with few labeled
nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of
M3S training method. Then we leverage DeepCluster technique, a popular form of
self-supervised learning, and design corresponding aligning mechanism on the
embedding space to refine the Multi-Stage Training Framework, resulting in M3S
Training Algorithm. Finally, extensive experimental results verify the superior
performance of our algorithm on graphs with few labeled nodes under different
label rates compared with other state-of-the-art approaches.

本文提出了一种名为 M3S 的多阶段自监督 (GCNs) 训练算法，该算法利用自监督学习方法，旨在提高 GCN 在缺乏标记节点的图上的泛化性能。实验结果表明，相比其他现有方法，我们的算法在不同标签率下表现出卓越的性能。