Graph Machine Learning, Second Edition not only revises but expands on its successful first edition, providing you with the latest tools and techniques in graph machine learning. This edition introduces comprehensive updates across all chapters, new chapters on trending topics like LLMs and Temporal Graph Learning, and real-world case studies that illustrate the practical applications of these concepts.
From basic graph theory to advanced machine learning models, the book guides you through understanding how data can be represented as graphs to uncover complex patterns and relationships hidden in your data. This edition emphasizes practical application with updated code examples using Pytorch Geometric, making it easier for you to implement what you learn.
The expanded content includes detailed chapters on using graph machine learning for dynamic and evolving data and integrating graph theory with Large Language Models (LLMs) for enriched data interaction and analysis. By the end of this book, you’ll not only be versed in the theory of graph machine learning but also adept at applying it to solve real challenges in innovative ways.

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Spis treści

Graph Machine Learning. Learn about the latest advancements in Graph data to build robust machine learning algorithms - Second Edition eBook -- spis treści

• 1. Getting Started with Graphs
• 2. Graph Machine Learning
• 3. Unsupervised Graph Learning
• 4. Supervised Graph Learning
• 5. Common Applications of Machine Learning on Graphs
• 6. Social Network Graphs
• 7. Text Analytics and Natural Language Processing (NLP) using Graphs
• 8. Graph Analysis for Credit Card Transactions
• 9. Building a data-driven Graph-Powered Application
• 10. Novel Trends on Graphs
• 11. LLMs and Graphs
• 12. Real-world scenarios (tentative)