Broader Engagement (BE): Introduction to Graph Neural Networks @SIAMConnect
Broader Engagement (BE): Introduction to Graph Neural Networks  @SIAMConnect
Uploaded March 2024 | Updated September 2026, 1 week ago
Graph Neural Networks (GNNs) are considered a subset of deep learning methods designed to make predictions on graph representations. Most practical applications come from the areas of physics simulations, object detection and recommendation systems. Given the extended application areas, GNNs are one of fastest growing and most active research topic that attracts increasing attention not only from the machine learning and data science community, but from the larger scientific community. The materials for this tutorial will be selected for researchers with no prior knowledge of GNNs. Further reading, applications and most popular software packages and frameworks will be discussed.

MT2: Broader Engagement (BE): Introduction to Graph Neural Networks
Organizer: Alina Lazar
Youngstown State University, U.S.

This talk was given at the 2022 SIAM Conference on Mathematics of Data Science in San Diego, California, U.S. Learn more about SIAM Conferences at siam.org/conferences/about-siam-conferences
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Society for Industrial and Applied Mathematics (SIAM) |

Broader Engagement (BE): Introduction to Graph Neural Networks

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