This tutorial aims to introduce dynamic graph neural networks (DGNNs) and their applications in modeling evolving graphs over time.
In the presentation session, you’ll learn the basics of Graph Neural Networks (GNNs), understand the difference between static and dynamic graphs, explore dynamic graph representation learning, and become familiar with key DGNN architectures. In the practical session, you’ll apply these concepts hands-on: preparing temporal graph data, implementing a DGNN model, training and evaluating it, and visualizing temporal node embeddings. By the end, you will be able to experiment with dynamic graphs and build models that capture temporal dynamics in data
Schedule: Theory 14:00 – 14:50 / Coffee Break 14:50 – 15:10 / Lab 15:10 – 16:00
Location: Nancy – Salle A008.
Instructor: Mohammed Khatbane, Ph.D. Candidate
