Graph representation learning is crucial for many real-world applications (eg social relation
analysis). A fundamental problem for graph representation learning is how to effectively …

Graph contrastive learning (GCL) has recently emerged as an effective learning paradigm to
alleviate the reliance on labelling information for graph representation learning. The core of …

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

Unsupervised graph-level representation learning has recently shown great potential in a
variety of domains, ranging from bioinformatics to social networks. Plenty of graph …

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

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 …

Graphs are powerful representations for relations among objects, which have attracted
plenty of attention in both academia and industry. A fundamental challenge for graph …

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

Graph representation learning via Contrastive Learning (GCL) has drawn considerable
attention recently. Efforts are mainly focused on gathering more global information via …

Graph contrastive learning (GCL) alleviates the heavy reliance on label information for
graph representation learning (GRL) via self-supervised learning schemes. The core idea is …