Graph Machine Learning, 2nd Edition
Graph Machine Learning, 2nd Edition is a Packt book on graph data analysis and machine learning, updated with topics including temporal graphs and the use of LLMs with graph data. Its examples feature PyTorch Geometric and DGL. The listing recommends prior knowledge of Python and basic machine learning.
At a glance
- Covers graph machine learning from graph fundamentals through supervised and unsupervised learning and applications.
- Adds chapters on temporal graph machine learning and graph-related uses of LLMs, including RAG.
- Examples use modern graph ML frameworks including PyTorch Geometric and DGL.
- Topics include graph-based text analytics, social networks, and transaction analysis.
- The listing says print or Kindle purchases include a free PDF eBook, with email sign-up and proof of purchase required.
Reference price: $54.99
Price checked: Nov 14, 2026
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Pros and cons
Pros
- Combines theoretical concepts with implementation-oriented examples and applied problem contexts.
- The coverage of temporal graphs and LLM-related workflows broadens the book beyond static graph learning.
- The framework coverage helps readers consider tools such as PyTorch Geometric and DGL.
Cons
- The book is not presented as a step-by-step tutorial, so readers seeking a guided beginner course may prefer a more procedural resource.
- The listing recommends familiarity with Python and basic machine learning, which may limit its usefulness as a first introduction.
Technical specifications
9 specifications| Publisher | Packt Publishing |
|---|---|
| Edition | 2nd ed. |
| Publication date | July 18, 2025 |
| Language | English |
| Print length | 434 pages |
| ISBN-10 | 1803248068 |
| ISBN-13 | 978-1803248066 |
| Dimensions | 7.5 x 0.98 x 9.25 inches |
|---|---|
| Weight listed | 1.63 pounds |
What to know before buying
- The free PDF and Packt Reader access have conditions. The listing requires email sign-up and proof of purchase for the stated digital access.
Who is it for?
A good fit for
- Data scientists and machine learning practitioners extending their work to graph data.
- Readers with Python and basic machine learning knowledge who want theory alongside applied graph ML examples.
- Graph specialists interested in temporal learning, graph-based text analysis, or graph and LLM workflows.
Less suited to
- Readers with no Python or machine learning background who need a fully guided introductory tutorial.
- Buyers looking specifically for a book focused only on one graph framework or a single application area.
Customer reviews on Amazon
- 5 stars
84% 84% - 4 stars
16% 16% - 3 stars
0% 0% - 2 stars
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Customer ratings are strongly positive overall, with the supplied distribution showing ratings in the top two star categories. This is a small aggregate snapshot and does not explain readers’ reasons. It offers limited guidance on whether the book’s balance of theory, framework examples, and newer topics suits a particular reader.
Frequently asked questions
What topics does Graph Machine Learning, 2nd Edition cover?
The listing describes graph fundamentals, graph neural networks, supervised and unsupervised graph learning, temporal graphs, graph-based text analytics, and graph applications involving LLMs.
Which frameworks are featured?
Examples include PyTorch Geometric and DGL. The listing also mentions StellarGraph in its learning objectives, while stating that the second edition updates its examples to modern alternatives.
Who is this book aimed at?
It is aimed at data scientists, machine learning professionals, and graph specialists. Prior knowledge of Python and basic machine learning is recommended.
Does the book include a PDF eBook?
The listing says print and Kindle purchases include a free PDF eBook. Email sign-up and proof of purchase are required for the stated digital access.
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