Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Alexander Möllers Alexander Immer Elvin Isufi Vincent Fortuin
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.