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.
This book covers the following exciting features:
* 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
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In order to build powerful graph and deep learning applications using PyTorch Geometric, the new book “Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch Geometric” provides a comprehensive guide. This book covers fundamental concepts of graph neural networks, implementation using Python and PyTorch Geometric, classification of nodes, graphs, and edges using millions of samples, prediction and generation of realistic graph topologies, combining heterogeneous sources to improve performance, forecasting future events using topological information, and applying graph neural networks to solve real-world problems.
To run the code examples in this book, you will need a basic understanding of Python programming, as well as machine learning concepts such as supervised and unsupervised learning, training, and evaluation of models. Basic knowledge of deep learning frameworks like PyTorch is also helpful but not essential, as the book provides a comprehensive introduction to mathematical concepts and their implementation using PyTorch Geometric.
To install the required software and hardware for this book, you will need Python 3.8.15 (or later), PyTorch 1.13.1 (or later), PyTorch Geometric 2.2.0 (or later), CUDA (optional but recommended if using a GPU), and cuDNN (if using CUDA). The book provides detailed instructions for installation on Windows, Mac OS X, and Linux systems.
Chapter 11 requires TensorFlow 2.4, which can be installed separately from the other required packages. Other Python libraries are also required in some or most chapters, but these can be easily installed using pip install or another package manager depending on your configuration.
The book provides a PDF file with color images of screenshots and diagrams used throughout the text. Additionally, you can download notebooks for Google Colab to run code examples directly from the cloud.