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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

Exercises

To practice what you have learned in this chapter, you can use the following ideas to explore your data:

  1. Try to build the street network of your own location. For this, you will need to find the central location coordinates (which you can do using Google Maps, for instance) and update the Geospatial_Network_Creation notebook.
  2. In Neo4j Bloom, use the Filter toolbox to visualize only nodes in the bigger Louvain community (use Cypher to find out the ID of the biggest community).
  3. Still in Bloom, configure the node color to be a function of its degree (the value stored in the degree property).
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