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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

Running GDS algorithms from Python and extracting data in a dataframe

In a preceding chapter, we learned that GDS algorithms offer multiple run modes, depending on where we want the results to be saved. In stream mode, the algorithm results are just streamed to the user, who has to decide what to do with them. In write mode, the results are persisted in the Neo4j database. Finally, mutate mode will update the in-memory projected graph with the results, which will be lost when the Neo4j instance is restarted, just like all the projected graphs. In this section, we will look at write and stream modes.

The code for the next paragraph is available in the Running_Algorithms_From_Python notebook.

write mode

As we just mentioned, when calling a GDS algorithm in write mode, the results of the algorithm computation will be written back to the main Neo4j graph. This is the only way to persist a result when the Neo4j server is restarted. The result can be either of the following:

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