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Hands-On Graph Analytics with Neo4j

You're reading from   Hands-On Graph Analytics with Neo4j Perform graph processing and visualization techniques using connected data across your enterprise

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Length 510 pages
Edition 1st Edition
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Author (1):
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 Scifo Scifo
Author Profile Icon Scifo
Scifo
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Graph Modeling with Neo4j
2. Graph Databases FREE CHAPTER 3. The Cypher Query Language 4. Empowering Your Business with Pure Cypher 5. Section 2: Graph Algorithms
6. The Graph Data Science Library and Path Finding 7. Spatial Data 8. Node Importance 9. Community Detection and Similarity Measures 10. Section 3: Machine Learning on Graphs
11. Using Graph-based Features in Machine Learning 12. Predicting Relationships 13. Graph Embedding - from Graphs to Matrices 14. Section 4: Neo4j for Production
15. Using Neo4j in Your Web Application 16. Neo4j at Scale 17. Other Books You May Enjoy

Extracting the data

Our nodes now have three more properties – wcc, pr, and lv – which we want to extract on top of the pre-existing properties. We can do so with the following Cypher code:

MATCH (u:User)
RETURN
u.contributed_to_neo4j as contributed_to_neo4j,
u.followers as followers,
u.publicRepos as publicRepos,
u.wcc as wcc,
u.pr as pr,
u.lv as lv

The result of this query can be downloaded as CSV directly from the browser.

This is useful when trying to build a model; however, this is not viable in a production environment. That's why the following section is dedicated to the automation of these steps, using the Neo4j Python driver.

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