Hands-On Graph Neural Networks using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks.The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data.As you advance, you’ll explore major graph