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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

Computing a node’s degree with GDS

We have studied the node degree metric and its distribution in the preceding chapter, Chapter 3, Characterizing a Graph Dataset. At that time, we computed the node’s degree using a Cypher query. GDS provides a procedure to perform the same computation, on a projected graph. We are now going to use this procedure, whose results are well known, in order to understand the different algorithm modes and configuration options.

All algorithm procedures from GDS use the same syntax:

gds.<algoName>.<executionMode>(<graphName>, <algoConfiguration>)

Here, the following applies:

  • algoName is the name of the algorithm. Note that some algorithms are included in an alpha or beta version, in which case they are accessible via gds.alpha.<algoName> or gds.beta.<algoName>.
  • executionMode is one of stream, write, mutate, estimate or stats, as defined in the GDS project workflow section.
  • graphName...
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