Graph Neural Networks in Action - MEAP V06

Description

A hands-on guide to powerful graph-based deep learning models! Learn how to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. Graph Neural Networks in Action teaches you to create powerful deep learning models for working with graph data. You’ll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. In Graph Neural Networks in Action you’ll create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data’s unique properties. Then, dive straight into building real-world models, including GNNs that can generate node embeddings from a social network, recommend eCommerce products, and draw insights from social sites. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale. In Graph Neural Networks in Action, you will learn how to: • Train and deploy a graph neural network • Generate node embeddings • Use GNNs at scale for very large datasets • Build a graph data pipeline • Create a graph data schema • Understand the taxonomy of GNNs • Manipulate graph data with NetworkX Copyright_2023_Manning_Publications welcome 1_Discovering_Graph_Neural_Networks 2_System_Design_and_Data_Pipelining 3_Graph_Embeddings 4_Graph_Convolutional_Networks_(GCNs)_&_GraphSage 5_Graph_Attention_Networks_(GATs) 6_Graph_Autoencoders Appendix_A._Discovering_Graphs