Graph property prediction tasks are important and numerous. While each task offers a small
size of labeled examples, unlabeled graphs have been collected from various sources and …

This paper focuses on graph-level representation learning that aims to represent graphs as
vectors that can be directly utilized in downstream tasks such as graph classification. We …

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph
learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input …

Graph contrastive learning (GCL), as an emerging self-supervised learning technique on
graphs, aims to learn representations via instance discrimination. Its performance heavily …

Abstract Graph Neural Networks (GNNs) are powerful tools for graph representation
learning. Despite their rapid development, GNNs also face some challenges, such as over …

Graph Neural Networks (GNNs) have achieved unprecedented success in identifying
categorical labels of graphs. However, most existing graph classification problems with …

Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Recent years have witnessed remarkable success achieved by graph neural networks
(GNNs) in many real-world applications such as recommendation and drug discovery …

Recently, contrastiveness-based augmentation surges a new climax in the computer vision
domain, where some operations, including rotation, crop, and flip, combined with dedicated …

Breast cancer is a major public health concern that affects women worldwide. It is a leading
cause of cancer-related deaths among women, and early detection is crucial for successful …