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 …

Deep graph learning has achieved remarkable progresses in both business and scientific
areas ranging from finance and e-commerce, to drug and advanced material discovery …

Graph Neural Networks (GNNs) have achieved promising results for semi-supervised
learning tasks on graphs such as node classification. Despite the great success of GNNs …

Despite the exploding interest in graph neural networks there has been little effort to verify
and improve their robustness. This is even more alarming given recent findings showing that …

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node
representations for various graph inference tasks. However, learning over graph data can …

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 garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …

Graph Neural Networks (GNNs) have become widely-used models for semi-supervised
learning. However, the robustness of GNNs in the presence of label noise remains a largely …

Node classification on graphs is a significant task with a wide range of applications,
including social analysis and anomaly detection. Even though graph neural networks …

Graph neural networks (GNNs) have been widely applied for representation learning on the
graph data in real applications, but few of them are designed to conduct representation …