Bayesian Graph Contrastive Learning
This addresses the limitation of existing methods in high-stakes domains by providing uncertainty estimates, though it is incremental as it builds on established graph contrastive learning techniques.
The paper tackles the problem of uncertainty quantification in graph contrastive learning by proposing a Bayesian method that represents nodes as distributions instead of deterministic vectors, resulting in improved performance on benchmark datasets.
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods are incapable of uncertainty quantification for node representations or their downstream tasks, limiting their application in high-stakes domains. In this paper, we propose a novel Bayesian perspective of graph contrastive learning methods showing random augmentations leads to stochastic encoders. As a result, our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. By learning distributional representations, we provide uncertainty estimates in downstream graph analytics tasks and increase the expressive power of the predictive model. In addition, we propose a Bayesian framework to infer the probability of perturbations in each view of the contrastive model, eliminating the need for a computationally expensive search for hyperparameter tuning. We empirically show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.