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。

面向基于图的半监督学习的伪对比学习