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 book book eBookKey FeaturesImplement stateoftheart graph neural network 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 social 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.HandsOn 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 youll explore major graph neural network architectures and learn essential concepts such as graph convolution selfattention link prediction and heterogeneous graphs.