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.

翻译：伪标签技术是一种通过基于高置信度预测生成额外伪标签来提升半监督图神经网络性能的方法。然而，由于分类目标对给定标签的敏感性，生成伪标签的质量长期以来一直受到关注。为避免不可靠的分类监督（即表明“某个节点属于特定类别”），我们更倾向于容错性更强的对比监督（即表明“两个节点不属于同一类别”）。这样，生成高质量伪标签的问题便被转化为一个更宽松的版本——寻找可靠的对比对。为此，我们提出了一种通用的图神经网络框架，称为伪对比学习（PCL）。该框架将正负伪标签指向同一类别的两个节点进行分离。为了将拓扑知识融入学习过程，我们设计了一种拓扑加权对比损失函数，该函数将更多精力用于分离拓扑距离较小的负样本对。此外，为减轻对数据增强的过度依赖，我们仅通过对编码表示应用丢弃法来增强节点。从理论上，我们证明了采用轻量级增强的PCL类似于表示正则化器，能够有效学习负样本对之间的分离。在实验中，我们将PCL应用于多种模型，在五个真实世界图上，这些模型的表现始终优于使用其他流行通用技术的对应模型。