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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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Why do we need embedding?

Machine learning models are based on matrix calculations: our observations are organised into rows in a table, while the features are columns or vectors. Representing complex objects such as text or graphs as matrices of a reasonable size can be a challenge. This is the issue that embedding techniques are designed to address.

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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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