Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
This addresses the challenge of limited supervision in graph learning tasks, which is a common issue in domains like social networks or bioinformatics, though it appears incremental as it builds on existing self-supervised techniques.
The paper tackles the problem of learning graph embeddings with few labeled nodes by proposing a Multi-Stage Self-Supervised (M3S) training algorithm for Graph Convolutional Networks, which shows superior performance compared to state-of-the-art methods under different label rates.
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.