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Graph Data Science with Neo4j

You're reading from   Graph Data Science with Neo4j Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project

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Product type Paperback
Published in Jan 2023
Publisher Packt
ISBN-13 9781804612743
Length 288 pages
Edition 1st Edition
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Author (1):
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Estelle Scifo Estelle Scifo
Author Profile Icon Estelle Scifo
Estelle Scifo
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Table of Contents (16) Chapters Close

Preface 1. Part 1 – Creating Graph Data in Neo4j
2. Chapter 1: Introducing and Installing Neo4j FREE CHAPTER 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

Practice with the Pregel API and write an algorithm. If you need some intermediate steps, here are a couple of exercises to help you get started:

  1. Update the Python implementation so that it computes the normalized PageRank given by the following formula:

Here, N is the total number of nodes in the graph.

Warning: Be careful with the score initialization.

  1. Again, using the Python implementation, take into account relationship weights. Hint: Relationship weights are entered into the outgoing degree part of the formula.
  2. Update the Java implementation to track the PR values at each step. We want to be able to see the evolution of PR at each iteration when calling the algorithm in Cypher.

This means adding a new field to our schema of the double[] type and extending it at each iteration.

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