Semi-supervised teaching graph contrastive network for node classification

Graph contrastive learning methods have recently emerged as a promising solution to tackle the problem of label scarcity in real-world scenarios. However, most of the existing methods are still flawed due to the lack of guided objectives. Moreover, they fail to effectively utilize complementary structural information from different graphs. To address these limitations, we propose a novel semi-supervised teaching graph contrastive network (STGCN) for node classification. Based on the teaching network architecture, STGCN establishes multi-level contrastive objectives, ensuring rich and detailed supervision for graph encoders. Specifically, after carefully analyzing the intrinsic correlation between different augmented views, we send a diffusion graph and two augmented views together into the novel teaching network, which owns one teacher encoder to guide two shared student encoders. Furthermore, we introduce a random sampling mixing module that extracts complementary information from multiple graphs, along with a label propagation technique to fully exploit limited labeled data. Finally, our method incorporates supervised contrastive loss and node similarity regularization to ensure coherent alignment between labeled and unlabeled nodes. Extensive experiments on five real-world node classification datasets demonstrate a maximum of 2.60% higher improvement than other models.