Hands-On Graph Neural Networks Using Python
- Paperback: 354 pages
- Publisher: WOW! eBook (April 14, 2023)
- Language: English
- ISBN-10: 1804617520
- ISBN-13: 978-1804617526
Hands-On Graph Neural Networks Using Python: Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps
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.
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 neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.
- Understand the fundamental concepts of graph neural networks
- Implement graph neural networks using Python and PyTorch Geometric
- Classify nodes, graphs, and edges using millions of samples
- Predict and generate realistic graph topologies
- Combine heterogeneous sources to improve performance
- Forecast future events using topological information
- Apply graph neural networks to solve real-world problems
By the end of this Hands-On Graph Neural Networks Using Python book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.