Hard negative mining has shown effective in enhancing self-supervised contrastive learning
(CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL …

Contrastive Learning (CL) has emerged as a dominant technique for unsupervised
representation learning which embeds augmented versions of the anchor close to each …

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning.
However, it suffers from sampling bias, which hinders its performance. While the mainstream …

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for
analyzing HGs, while most of them rely on a relative large number of labeled data. In this …

Graph contrastive learning (GCL) has attracted rising research attention recently due to its
effectiveness in self-supervised graph learning. A key step of GCL is to conduct data …

Unsupervised contrastive learning has recently become increasingly popular due to its
amazing performance without the need for costly annotations. However, indiscriminate …

Graph Neural Networks (GNNs) have emerged as the predominant approach for analyzing
graph data on the web and beyond. Contrastive learning (CL), a self-supervised paradigm …

Graph contrastive learning has emerged as a powerful unsupervised graph representation
learning tool. The key to the success of graph contrastive learning is to acquire high-quality …

Contrastive learning is an effective unsupervised method in graph representation learning.
The key component of contrastive learning lies in the construction of positive and negative …

Graph contrastive learning (GCL) is one of the mainstream techniques for unsupervised
graph representation learning, which reduces the distance between positive pairs and …