Pseudo Labeling is a technique used to improve the performance of
semi-supervised Graph Neural Networks (GNNs) by generating additional
pseudo-labels based on confident predictions. However, the quality of generated
pseudo-labels has long been a concern due to the sensitivity of the
classification objective to given labels. To avoid the untrustworthy
classification supervision indicating ``a node belongs to a specific class,''
we favor the fault-tolerant contrasting supervision demonstrating ``two nodes
do not belong to the same class.'' Thus, the problem of generating high-quality
pseudo-labels is then transformed into a relaxed version, i.e., finding
reliable contrasting pairs. To achieve this, we propose a general framework for
GNNs, termed Pseudo Contrastive Learning (PCL). It separates two nodes whose
positive and negative pseudo-labels target the same class. To incorporate
topological knowledge into learning, we devise a topologically weighted
contrastive loss that spends more effort separating negative pairs with smaller
topological distances. Additionally, to alleviate the heavy reliance on data
augmentation, we augment nodes only by applying dropout to the encoded
representations. Theoretically, we prove that PCL with the lightweight
augmentation works like a representation regularizer to effectively learn
separation between negative pairs. Experimentally, we employ PCL on various
models, which consistently outperform their counterparts using other popular
general techniques on five real-world graphs.

该论文提出了一种 Pseudo Contrastive Learning (PCL) 的通用框架，该框架通过生成可靠的对比对来解决生成高质量伪标签的问题，通过对负样本进行 dropout 来实现数据增强。 最终，实验证明该方法在五个真实世界的图上均优于其他普遍技术。