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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

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781839212611

Length 510 pages

Edition 1st Edition

Languages

Cypher

Tools

Neo4j

Concepts

Database Programming

Author (1):

Scifo

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Table of Contents (18) Chapters

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

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Using local neighborhood information

In order to understand the mathematics behind the following formulas, let's first introduce some notations, similar to the ones used in Chapter 7, Community Detection and Similarity Measures:

u, v, and i are nodes of the graph.
N(u) denotes the set of neighbors of node u.
|N(u)| is the size of the set, meaning the number of neighbors of node u.

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Authors (1)

Scifo

Scifo

Estelle Scifo possesses over 7 years experience as a data scientist, after receiving her PhD from the Laboratoire de lAcclrateur Linaire, Orsay (affiliated to CERN in Geneva). As a Neo4j certified professional, she uses graph databases on a daily basis and takes full advantage of its features to build efficient machine learning models out of this data. In addition, she is also a data science mentor to guide newcomers into the field. Her domain expertise and deep insight into the perspective of the beginners needs make her an excellent teacher.

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