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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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Toc

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

Testing our code

GDS also provides a utility for writing unit tests for our code. We are going to test both implementations, PageRank and PageRankTol, starting with the former.

Test for the PageRank class

Let’s get started and detail the code block by block:

  1. First, we must define our test class, called PageRankTest:
    @GdlExtension
    class PageRankTest {
  2. Then, we must create a graph object from a Cypher string, using NATURAL orientation for the relationship (the default one):
       @GdlGraph(orientation = Orientation.NATURAL)
        private static final String TEST_GRAPH =
                "CREATE" +
                        "  (A:Node)" +
                        &quot...
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