  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 been a longstanding concern due to the sensitivity of the
classification objective with respect to the 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., identifying reliable negative 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. Experimentally, we apply PCL
to various GNNs, which consistently outperform their counterparts using other
popular general techniques on five real-world graphs.
