Abstract Graph Contrastive Learning (GCL) has drawn much research interest due to its
strong ability to capture both graph structure and node attribute information in a self …

In contrastive learning, the choice of" view" controls the information that the representation
captures and influences the performance of the model. However, leading graph contrastive …

Recent years have seen a growing interest in Graph Contrastive Learning (GCL), which
trains Graph Neural Network (GNN) model to discriminate similar and dissimilar pairs of …

Graph contrastive learning (GCL) has emerged as a dominant technique for graph
representation learning which maximizes the mutual information between paired graph …

Abstract Graph Contrastive Learning (GCL) has shown superior performance in
representation learning in graph-structured data. Despite their success, most existing GCL …

Various graph contrastive learning models have been proposed to improve the performance
of tasks on graph datasets in recent years. While effective and prevalent, these models are …

Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

Existing graph contrastive learning methods rely on augmentation techniques based on
random perturbations (eg, randomly adding or dropping edges and nodes). Nevertheless …

Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph
representations without human annotations. Although remarkable progress has been …

Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA
on various downstream tasks. The graph augmentation step is a vital but scarcely studied …