Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation into account. We show that a variational Bayesian neural network approach can be used to improve not only the uncertainty estimates but also the downstream performance on semi-supervised node-classification tasks. Moreover, we propose a new measure of uncertainty for contrastive learning, that is based on the disagreement in likelihood due to different positive samples.

翻译：图对比学习在标注数据稀缺但大量未标注数据可用时展现出巨大潜力。然而，该方法通常未考虑不确定性估计。研究表明，变分贝叶斯神经网络方法不仅能改善不确定性估计，还能提升半监督节点分类任务的下游性能。此外，我们提出了一种新的对比学习不确定性度量方法，该方法基于不同正样本引起的似然不一致性。