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

Graph representation is an important part of graph clustering. Recently, contrastive learning,
which maximizes the mutual information between augmented graph views that share the …

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

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, which to date has always been guided by node features and
fixed-intrinsic structures, has become a prominent technique for unsupervised graph …

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 …

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 …

Graph representation learning nowadays becomes fundamental in analyzing graph-
structured data. Inspired by recent success of contrastive methods, in this paper, we propose …