Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

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 …

Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for
downstream tasks with unlabeled data, and it has recently become an active research area …

In recent years, there have been remarkable advancements in node classification achieved
by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels …

Deep learning (DL) techniques have shown remarkable progress in remotely sensed
hyperspectral image (HSI) classification tasks. The performance of DL-based models highly …

Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …

Graph classification aims to predict the class labels of graphs and has a wide range of
applications in many real-world domains. However, most of existing graph neural networks …

Graph Convolutional Network (GCN) has exhibited strong empirical performance in many
real-world applications. The vast majority of existing works on GCN primarily focus on the …

Graph neural networks (GNNs) have been attracting increasing popularity due to their
simplicity and effectiveness in a variety of fields. However, a large number of labeled data is …

Data selection methods, such as active learning and core-set selection, are useful tools for
improving the data efficiency of deep learning models on large-scale datasets. However …