Towards Graph Contrastive Learning: A Survey and Beyond
May 20, 2024 · We provide a comprehensive overview of the fundamental principles of GCL, including data augmentation strategies, contrastive modes, and contrastive optimization …
Graph contrastive learning with node-level accurate difference
Mar 1, 2025 · Graph contrastive learning (GCL) has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self …
Graph Contrastive Learning via Interventional View Generation
May 13, 2024 · Graph contrastive learning (GCL), as a popular self-supervised learning technique, has demonstrated promising capability in learning discriminative representations for …
Graph Contrastive Learning | Michael Plainer
May 15, 2023 · Graph contrastive learning is a technique to learn representations in a self-supervised setting by exploiting the graph’s structure with augmentations to create contrastive …
Graph Contrastive Learning (GCL), a Self-Supervised Learning (SSL) architecture tailored for graphs, has shown notable potential for mitigating label scarcity. Its core idea is to amplify …
A Good View for Graph Contrastive Learning - MDPI
Feb 27, 2024 · Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily …
Revisiting Graph Contrastive Learning from the Perspective of Graph …
Graph Contrastive Learning (GCL), learning the node representations by augmenting graphs, has attracted considerable attentions. Despite the proliferation of various graph augmentation …
Simplified Graph Contrastive Learning Model Without …
In this paper, we propose one simplified GCL model to simultaneously address these problems via the minimal components of a general graph contrastive framework, i.e., a GNN encoder …
A typical Graph Contrastive Learning (GCL) method constructs multiple graph views via stochastic augmentations of the input at first and then learns representations by contrasting positive …
Graph contrastive learning via coarsening: A time and memory …
May 23, 2025 · We propose CGCL method, a graph contrastive learning model based on graph coarsening, which can train an encoder with lower time and memory costs by implementing …