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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
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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

Getting the data

The dataset we are going to use now was created from data published by the city of New York. You can download it from the GitHub repository associated with this book, under ch5/data/mahattan_district.shp. More information about how this file was created can also be found at the same location.

The data file is in a shapefile format, a format very common among spatial data professionals. It contains the boundaries and name of each community district within Manhattan in New York. The following image shows the districts we are going to work with:

The 11 districts of the borough of Manhattan in New York. Image created using QGIS

Any GIS is able to decode the shapefile format, as is neo4j-spatial. The first step of our analysis with Neo4j is to import the data into our graph.

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