Textbook in PDF format Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric, and DGL Key Features Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL) Explore GML frameworks and their main characteristics Leverage LLMs for machine learning on graphs and learn about temporal learning Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. Getting Started with Graphs Graph Machine Learning Neural Networks and Graphs Unsupervised Graph Learning Supervised Graph Learning Solving Common Graph-Based Machine Learning Problems Social Network Graphs Text Analytics and Natural Language Processing Using Graphs Graph Analysis for Credit Card Transactions Building a Data-Driven Graph-Powered Application Temporal Graph Machine Learning GraphML and LLMs Novel Trends on Graphs