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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

Dealing with spatial data in Neo4j

Neo4j has a built-in type for dealing with spatial data, but only for points so far (lines and polygons are not supported (yet)).

Wikidata contains coordinates information for many entities. For instance, each country has a location. We can extract it using the following query:

SELECT ?country ?countryLabel ?lat ?lon
WHERE {
  ?country rdfs:label "India"@en;
                 wdt:P31 wd:Q6256.
     ?country p:P625 ?coordinate.
     ?coordinate psv:P625 ?coordinate_node.
     ?coordinate_node wikibase:geoLongitude ?lon.
     ?coordinate_node wikibase:geoLatitude ?lat.
  SERVICE wikibase:label {bd:serviceParam wikibase:language "en" .}
}

Going further

Explanations of the new parts of this query are available here: https...

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