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Dive into the world of Graph Neural Networks (GNNs) in this comprehensive tutorial designed for researchers with no prior knowledge of the subject. Explore how GNNs, a subset of deep learning methods, make predictions on graph representations and their applications in physics simulations, object detection, and recommendation systems. Discover why GNNs are one of the fastest-growing and most active research topics, attracting attention from both the machine learning and data science communities, as well as the broader scientific community. Learn about the fundamental concepts, extended application areas, further reading materials, and popular software packages and frameworks used in GNN research. This 1-hour and 56-minute session, organized by Alina Lazar from Youngstown State University, was presented at the 2022 SIAM Conference on Mathematics of Data Science in San Diego, California, providing an excellent introduction to this cutting-edge field of study.
