An Introduction to Graph Neural Networks
Alina Lazar
Abstract
Graph Neural Networks (GNNs) are considered a subset of deep learning methods designed to extract important information and make useful predictions on graph representations. Researchers have been working to adapt neural networks to operate on graph data for more than a decade. 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 as well. The materials for this tutorial will be selected and organized for researchers with no prior knowledge of GNNs. Further reading, applications and most popular software packages and frameworks will also be discussed.