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 …

Graphs have been widely used in data mining and machine learning due to their unique
representation of real-world objects and their interactions. As graphs are getting bigger and …

There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for
semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled …

Pedestrian trajectory prediction is an essential component in a wide range of AI applications
such as autonomous driving and robotics. Existing methods usually assume the training and …

Network anomaly detection, also known as graph anomaly detection, aims to find network
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have
facilitated various applications from drug discovery to recommender systems. Nevertheless …

Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …

Graph neural networks (GNNs) have achieved superior performance in various applications,
but training dedicated GNNs can be costly for large-scale graphs. Some recent work started …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …