Explore the updated edition with new chapters on LLMs, Temporal Graphs, and updated Pytorch Geometric examples to enhance your data science skills.

Key Features

• Master new graph ML techniques with updated Pytorch Geometric examples
• Explore case studies that demonstrate real-world applications of GML
• Leverage graphs for advanced tasks in LLMs and Temporal learning

Book Description

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.

What you will learn

• Implement graph ML algorithms with some examples in PyTorch Geometric
• Apply graph analysis to dynamic datasets using Temporal Graph ML
• Enhance NLP and text analytics with graph-based techniques
• Solve complex real-world problems with graph machine learning
• Build and scale graph-powered ML applications effectively
• Deploy and scale out your application seamlessly

Who this book is for

This book is ideal for data scientists, ML professionals, and graph specialists looking to deepen their knowledge of graph data analysis or expand their machine learning toolkit. Prior knowledge of Python and basic machine learning principles is recommended.