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Hands-On Graph Analytics with Neo4j

You're reading from  Hands-On Graph Analytics with Neo4j

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Pages 510 pages
Edition 1st Edition
Languages
Author (1):
Estelle Scifo Estelle Scifo
Profile icon Estelle Scifo

Table of Contents (18) Chapters

Preface 1. Section 1: Graph Modeling with Neo4j
2. Graph Databases 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

Creating a full-stack web application using Python and Graph Object Mappers

There are different ways to programmatically interact with Neo4j. In the previous chapters, we used the Neo4j Python driver, from which we have been able to execute Cypher queries and retrieve the results, especially for creating DataFrames in a data science context. In the context of a web application, manually writing a Cypher query each time we want our program to perform an action on the graph would be a very time-consuming and laborious task involving repeating code. Fortunately, Graph Object Mappers (GOMs) have been created to interface Python code to Neo4j without us having to write a single Cypher query. In this section, we are going to use the neomodel package and use it alongside Flask to build a web application that displays information from Neo4j.

Our context is similar to GitHub: we have users that can own and/or contribute to repositories.

Toying with neomodel

neomodel is a Graph Object Mapper for...

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