Abstract: Anomaly detection in graphs is increasingly used to reveal fraud, fakes, security attacks and unusual behaviours in networks, such as social networks, financial transaction networks and the ...

Abstract: Handling noisy data is a longstanding challenge in machine learning, and the complexity increases when working with graph structured data. In domains such as social networks, biological ...

Deep learning has achieved remarkable success in graph-related tasks, yet this accomplishment heavily relies on large-scale high-quality annotated datasets. However, acquiring such datasets can be ...

This project demonstrates technical capability in fraud detection using Graph Neural Networks, supporting an NIW (National Interest Waiver) green card application. It implements the methodology ...