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Graph Machine Learning: Learn about the latest advancements in graph data to build robust machine learning models
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 FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph 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 This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.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.
What you will learnImplement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGLApply graph analysis to dynamic datasets using temporal graph MLEnhance NLP and text analytics with graph-based techniquesSolve complex real-world problems with graph machine learningBuild and scale graph-powered ML applications effectivelyDeploy and scale your application seamlesslyWho this book is forThis book is 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.
Table of ContentsGetting Started with GraphsGraph Machine LearningNeural Networks and GraphsUnsupervised Graph LearningSupervised Graph LearningSolving Common Graph-Based Machine Learning ProblemsSocial Network GraphsText Analytics and Natural Language Processing Using GraphsGraph Analysis for Credit Card TransactionsBuilding a Data-Driven Graph-Powered ApplicationTemporal Graph Machine Learning GraphML and LLMsNovel Trends on Graphs
Key FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph 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.
What you will learnImplement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGLApply graph analysis to dynamic datasets using temporal graph MLEnhance NLP and text analytics with graph-based techniquesSolve complex real-world problems with graph machine learningBuild and scale graph-powered ML applications effectivelyDeploy and scale your application seamlesslyWho this book is forThis book is 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.
Table of ContentsGetting Started with GraphsGraph Machine LearningNeural Networks and GraphsUnsupervised Graph LearningSupervised Graph LearningSolving Common Graph-Based Machine Learning ProblemsSocial Network GraphsText Analytics and Natural Language Processing Using GraphsGraph Analysis for Credit Card TransactionsBuilding a Data-Driven Graph-Powered ApplicationTemporal Graph Machine Learning GraphML and LLMsNovel Trends on Graphs
666 pages, Kindle Edition
Published July 18, 2025
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July 29, 2025
really enjoy reading this book. It really dives deeper into how graph machine learning can help capture pattern and relationship in our data and do various prediction-based approach to help make informed decision-making. i enjoy the use cases chapters in this book that focus on hands-on approach to utilize graph ML on fraud detection problem(tabular data) namely processing the data to form graph structure, analyze the structure to make prediction and some advices regarding the results.
Displaying 1 of 1 review