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.

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.

By the end of this 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.

What you will learn

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

Who this book is for

This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Table of Contents

Getting Started with Graph Learning

Graph Theory for Graph Neural Networks

Creating Node Representations with DeepWalk

Improving Embeddings with Biased Random Walks in Node2Vec

Including Node Features with Vanilla Neural Networks

Introducing Graph Convolutional Networks

Graph Attention Networks

Scaling Graph Neural Networks with GraphSAGE

Defining Expressiveness for Graph Classification

Predicting Links with Graph Neural Networks

Generating Graphs Using Graph Neural Networks

Learning from Heterogeneous Graphs

Temporal Graph Neural Networks

Explaining Graph Neural Networks

Forecasting Traffic Using A3T-GCN

Detecting Anomalies Using Heterogeneous Graph Neural Networks

Building a Recommender System Using LightGCN

Unlocking the Potential of Graph Neural Networks for Real-Word Applications