Speaker
Dr
Aurora Rossi
(Université Côte d’Azur)
Description
Graph Neural Networks have been very successful in solving machine learning tasks such as classification and prediction at the level of nodes, edges, and graphs. This talk will provide a comprehensive overview of GNN architectures, from standard graphs to heterogeneous graphs, which model various relationships among different types of nodes, and temporal graphs, which incorporate dynamic changes in relationships over time. We will present practical applications in various domains demonstrating their versatility and impact.
