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 apps

Key Features

• Implement state-of-the-art graph neural network architectures in Python
• Create your own graph datasets from tabular data
• Build powerful traffic forecasting, recommender systems, and anomaly detection applications

Book Description

Graph 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.