Dec 15, 2021 · In this paper, we propose a novel Bayesian perspective of graph contrastive learning methods showing random augmentations leads to stochastic ...
Oct 25, 2022 · The key idea is to leverage Bayesian nonparametric methods to infer a subset of robust nodes and then conduct prototypical contrastive learning.
A novel Bayesian perspective of graph contrastive learning methods showing random augmentations leads to stochastic encoders is proposed, which represents ...
Aug 28, 2022 · In this paper, we propose a novel Bayesian perspective of graph contrastive learning methods showing random augmentations leads to stochastic ...
We propose a novel contrastive learning based on node behavior to capture the interaction changes between entities.
In this work, we propose a novel and robust method, Bayesian Robust Graph Contrastive Learning (BRGCL), which trains a GNN encoder to learn robust node ...
A novel and robust method, Bayesian Robust Graph Contrastive Learning (BRGCL), which trains a GNN encoder to learn robust node representations and ...
In this work, we propose a novel and robust method, Bayesian Robust Graph Contrastive Learning (BRGCL), which trains a GNN encoder to learn robust node ...
Abstract. Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available.
In this paper, we propose a novel and robust method termed Bayesian Robust Graph Contrastive Learning (BRGCL) to improve the robustness of node representations ...