Graph Machine Learning - Second Edition

Graph Machine Learning, Second Edition dives deep into the intersection of graph theory and machine learning, providing comprehensive discussions and practical examples using modern tools like PyTorch Geometric and DGL. By integrating large language models (LLMs) and introducing temporal learning, it equips you with cutting-edge know-how to analyze datasets efficiently and solve real-world problems.

What this Book will help me do

• Implement practical graph ML models using PyTorch Geometric and Deep Graph Library (DGL).
• Analyze dynamic data effectively with temporal graph machine learning techniques.
• Leverage large language models (LLMs) to optimize graph-based machine learning.
• Design and deploy scalable graph-empowered machine learning applications.
• Apply graph theory and neural networks to real-world challenges, such as credit card fraud detection.

Author(s)

The authors, Aldo Marzullo, Enrico Deusebio, and Claudio Stamile, bring extensive expertise in machine learning and data science. They combine academic foundations with industry experience to provide practical insights. Their understanding of graph-based algorithms, paired with a commitment to sharing knowledge, makes this book an invaluable resource for exploring new areas in machine learning.

Who is it for?

This book is ideal for data scientists and machine learning professionals interested in graph theory applications. If you are looking to expand your expertise to include graph machine learning, this book will guide you. Proficiency in Python and familiarity with machine learning concepts will help you get the most from this content. It's perfect for those striving to innovate with modern tools and theoretical foundations.