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Graph Data Science with Neo4j

You're reading from  Graph Data Science with Neo4j

Product type Book
Published in Jan 2023
Publisher Packt
ISBN-13 9781804612743
Pages 288 pages
Edition 1st Edition
Languages
Author (1):
Estelle Scifo Estelle Scifo
Profile icon Estelle Scifo

Table of Contents (16) Chapters

Preface 1. Part 1 – Creating Graph Data in Neo4j
2. Chapter 1: Introducing and Installing Neo4j 3. Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph 4. Part 2 – Exploring and Characterizing Graph Data with Neo4j
5. Chapter 3: Characterizing a Graph Dataset 6. Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset 7. Chapter 5: Visualizing Graph Data 8. Part 3 – Making Predictions on a Graph
9. Chapter 6: Building a Machine Learning Model with Graph Features 10. Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning 11. Chapter 8: Building a GDS Pipeline for Node Classification Model Training 12. Chapter 9: Predicting Future Edges 13. Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java 14. Index 15. Other Books You May Enjoy

Summary

In this chapter, you have learned the basic principles of the Neo4j GDS library 2.x. You have been able to create projected graphs, configuring included nodes, relationships, and properties with native graph projection. You have also learned how to generate properties or relationships on the fly using Cypher projections. In the second section, you have run your first GDS algorithm—the degree algorithm—and got familiar with the stream, write, and mutate algorithm modes. You have also been made aware of the algorithm configuration, especially regarding relationship orientation.

Once GDS had no more secrets to you, we started using other types of algorithms—namely, community detection algorithms. We studied a few of them and learned about their differences and what they can teach us about our graph.

In the next chapter, we will learn how to use another powerful tool of the Neo4j universe: Neo4j Bloom, yet another graph application. Bloom is designed...

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