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Graph Machine Learning

You're reading from   Graph Machine Learning Learn about the latest advancements in graph data to build robust machine learning models

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781803248066
Length 434 pages
Edition 2nd Edition
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Authors (3):
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Aldo Marzullo Aldo Marzullo
Author Profile Icon Aldo Marzullo
Aldo Marzullo
Enrico Deusebio Enrico Deusebio
Author Profile Icon Enrico Deusebio
Enrico Deusebio
Claudio Stamile Claudio Stamile
Author Profile Icon Claudio Stamile
Claudio Stamile
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Introduction to Graph Machine Learning
2. Getting Started with Graphs FREE CHAPTER 3. Graph Machine Learning 4. Neural Networks and Graphs 5. Part 2: Machine Learning on Graphs
6. Unsupervised Graph Learning 7. Supervised Graph Learning 8. Solving Common Graph-Based Machine Learning Problems 9. Part 3: Practical Applications of Graph Machine Learning
10. Social Network Graphs 11. Text Analytics and Natural Language Processing Using Graphs 12. Graph Analysis for Credit Card Transactions 13. Building a Data-Driven Graph-Powered Application 14. Part 4: Advanced topics in Graph Machine Learning
15. Temporal Graph Machine Learning 16. GraphML and LLMs 17. Novel Trends on Graphs 18. Index
19. Other Books You May Enjoy

Selecting the right technology

Neo4j or GraphX? This is a question that often gets asked. However, as we have described briefly, the two pieces of software are not really competitors, but they rather target different needs. Neo4j, Neptune, JanusGraph, and CosmoDB allow us to store information in a graph-like structure and query the data, whereas GraphX, Neo4j Graph Data Science, and Amazon Neptune Analytics make it possible to analytically process a graph (especially for large graph dimensions). Although you could also use Neo4j as a processing engine and GraphX could also be used as an in-memory stored graph, this approach should be discouraged due to performance limitations, scalability concerns, and mismatched feature sets.

Graph processing engines usually compute KPIs that get stored in the graph database layers (potentially indexed such that querying and sorting become efficient) for later use. Thus, technologies such as GraphX are not competing with graph databases such...

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