Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Deep learning's performance has been extensively recognized recently. Graph neural
networks (GNNs) are designed to deal with graph-structural data that classical deep …

Recently, molecular data mining has attracted a lot of attention owing to its great application
potential in material and drug discovery. However, this mining task faces a challenge posed …

Robust learning on graph data is an active research problem in data mining field. Graph
Neural Networks (GNNs) have gained great attention in graph data representation and …

Graph contrastive learning (GCL) has become a powerful tool for learning graph data, but its
scalability remains a significant challenge. In this work, we propose a simple yet effective …

Graph neural networks (GNNs) are powerful models for processing graph data and have
demonstrated state-of-the-art performance on many downstream tasks. However, existing …

Mandarin Chinese, a globally prevalent language, boasts an abundance of regularly
refreshed short news texts accessible online. Consequently, devising concise summaries of …

Abstract Graph Neural Networks (GNNs) are widely employed to derive meaningful node
representations from graphs. Despite their success, deep GNNs frequently grapple with the …

Data augmentation is a series of techniques that generate high-quality artificial data by
manipulating existing data samples. By leveraging data augmentation techniques, AI …

In previous research, the prevailing assumption was that Graph Neural Networks (GNNs)
precisely depicted the interconnections among nodes within the graph's architecture …