Description: This session introduces Graph Neural Networks (GNNs) and their innovative approach to understanding complex graphs. Dive into the core concepts—from graph representations and message passing to pooling and cutting-edge attention mechanisms. Using PyTorch Geometric, you’ll gain practical experience building, training, and optimizing GNN models on real-world datasets spanning molecular structures, social networks, and beyond.
Date: 14 May 14:00 - 14:50 (Theory) / 14:50 - 15:10 (Coffee Break) / 15:10 - 16:00 (Hands-on Lab)
Instructor: Victor Pryakhin, Ph.D. Candidate
Where? Nancy-Salle A008 Jean Legras
Certificates of participation available under request!
The tutorial will be held in English.
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