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 …

Despite the widespread use of graph neural networks (GNNs) we lack methods to reliably
quantify their uncertainty. We propose a conformal procedure to equip GNNs with prediction …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …

Rare categories abound in a number of real-world networks and play a pivotal role in a
variety of high-stakes applications, including financial fraud detection, network intrusion …

Due to the emergence of graph neural networks (GNNs) and their widespread
implementation in real-world scenarios, the fairness and privacy of GNNs have attracted …

Class imbalance is prevalent in real-world node classification tasks and often biases graph
learning models toward majority classes. Most existing studies root from a node-centric …

Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …

Graph neural networks (GNNs) are powerful tools on graph data. However, their predictions
are mis-calibrated and lack interpretability, limiting their adoption in critical applications. To …

Graph Neural Networks (GNNs) have seen significant success in tasks such as node
classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the …

Graph neural networks (GNNs) have gained significant attraction due to their expansive real-
world applications. To build trustworthy GNNs, two aspects-fairness and privacy-have …