Marzullo / Deusebio / Stamile Graph Machine Learning

Learn about the latest advancements in graph data to build robust machine learning models
1. Auflage 2025
ISBN: 978-1-80324-661-1
Verlag: Packt Publishing
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 434 Seiten

Graph Machine Learning, Second Edition builds on its predecessor's success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you'll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.
The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools.
By the end of this book, you'll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.

Autoren/Hrsg.

Marzullo, Aldo

Deusebio, Enrico

Stamile, Claudio

Leseproben

Preface

This updated and expanded second edition brings several significant improvements to help you stay ahead in the evolving field of graph machine learning. Compared to the previous version, this edition features refined chapters for improved clarity and flow, new examples utilizing both legacy tools and modern frameworks such as PyTorch and DGL, and entirely new chapters covering cutting-edge topics such as temporal graph machine learning and the integration of large language models (LLMs).

provides a powerful toolkit for processing network-structured data and leveraging the relationships between entities for predictive modeling, analytics, and more. You’ll begin with a concise introduction to graph theory, graph machine learning, and neural networks, building a foundational understanding of their principles and applications. As you progress, you’ll dive into the core machine learning models for graph representation learning, exploring their goals, inner workings, and practical implementation across various supervised and unsupervised tasks. You’ll develop an end-to-end machine learning pipeline, from data preprocessing to training and prediction, to fully harness the potential of graph data. Throughout the book, you’ll find real-world scenarios such as social network analysis, natural language processing with graphs, and financial transaction systems. The later chapters take you through the creation of scalable, data-intensive applications for storing, querying, and processing graph data and introduce you to the recent breakthroughs and emerging trends in the domain, some of which are the interaction between graphs and LLMs used in the context of generative AI and retrieval-augmented generation (RAG) systems.

By the end of this book, you will have understood the key concepts of graph theory and machine learning algorithms, allowing you to develop impactful graph-based machine learning solutions.

Who this book is for

This book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points, unravel hidden structures, and exploit topological information to boost their analysis and models’ performance. The book will also be useful for data scientists and machine learning developers who want to build machine learning-driven graph databases.

What this book covers

, , introduces the basic concepts of graph theory using the NetworkX Python library.

, , introduces the main concepts of graph machine learning and graph embedding techniques.

, , introduces Graph Neural Networks (GNNs) and the leading libraries for graph-based deep learning.

, , covers recent unsupervised graph embedding methods.

, , covers recent supervised graph embedding methods.

, , introduces the most common machine learning tasks on graphs.

, , shows an application of machine learning algorithms on social network data.

, , shows an application of machine learning algorithms on a natural language processing task.

, , shows an application of machine learning algorithms in credit card fraud detection.

, , introduces some technologies and techniques useful to deal with large graphs.

, , focuses on techniques to model and learn from dynamic, time-evolving graph data.

, , explores how graph structures can enhance LLMs and how LLMs can be used for graph-based tasks.

, , introduces some novel trends (algorithms and applications) of graph machine learning.

To get the most out of this book

We recommend that you use Docker to have a reproducible environment and stable dependency sets. The provided Docker images – one for each chapter – ship with a Jupyter installation and a Python kernel with the dependencies pre-installed, which you can use to run all the examples. For some chapters, Neo4j, JanusGraph, and Gephi are also needed.

Software/hardware covered in the book

OS requirements

Python

Windows, macOS, and Linux (any)

Neo4j

Windows, macOS, and Linux (any)

Gephi

Windows, macOS, and Linux (any)

Docker

Windows, macOS, and Linux (any)

A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most of this book.

The authors acknowledge the use of cutting-edge AI, such as ChatGPT, with the sole aim of enhancing the language and clarity within the book, thereby ensuring a smooth reading experience for readers. It’s important to note that the content itself has been crafted by the authors and edited by a professional publishing team.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Graph-Machine-Learning. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and X/Twitter handles. For example: “For this exercise, we will be using a GraphSAGE encoder with three layers of 32, 32, and 16 dimensions, respectively.”

A block of code is set as follows:

Any command-line input or output is written as follows:

Warnings or important notes appear like this.

Tips and tricks appear like this.

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