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

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

Recently, contrastive learning (CL) has emerged as a successful method for unsupervised
graph representation learning. Most graph CL methods first perform stochastic augmentation …

Recent studies show that graph convolutional network (GCN) often performs worse for low-
degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed …

Generalizable, transferrable, and robust representation learning on graph-structured data
remains a challenge for current graph neural networks (GNNs). Unlike what has been …

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

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) …

Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised
learning on graphs, which generally follows the" augmenting-contrasting''learning scheme …

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 drawn much research interest due to its
strong ability to capture both graph structure and node attribute information in a self …