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Graph Data Modeling in Python

You're reading from Graph Data Modeling in Python A practical guide to curating, analyzing, and modeling data with graphs

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781804618035

Length 236 pages

Edition 1st Edition

Languages

Python

Tools

Neo4j

Concepts

Data Analysis

Authors (2):

Gary Hutson

Matt Jackson

View More author details

Table of Contents (16) Chapters

Preface

1. Part 1: Getting Started with Graph Data Modeling

2. Chapter 1: Introducing Graphs in the Real World FREE CHAPTER

3. Chapter 2: Working with Graph Data Models

4. Part 2: Making the Graph Transition

5. Chapter 3: Data Model Transformation – Relational to Graph Databases

6. Chapter 4: Building a Knowledge Graph

7. Part 3: Storing and Productionizing Graphs

8. Chapter 5: Working with Graph Databases

9. Chapter 6: Pipeline Development

10. Chapter 7: Refactoring and Evolving Schemas

11. Part 4: Graphing Like a Pro

12. Chapter 8: Perfect Projections

13. Chapter 9: Common Errors and Debugging

14. Index

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15. Other Books You May Enjoy

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Authors (2)

Gary Hutson

Gary Hutson

Gary Hutson is an experienced Python and graph database developer. He has experience in Python, R, C, SQL, and many other programming languages, and has been working with databases of some form for 20+ years. Professionally, he works as the Head of Graph Data Science and Machine Learning for a company that uses machine learning (ML) and graph data science techniques to detect risks on social media and other platforms. He is experienced in many graph and ML techniques, specializing in natural language processing, computer vision, deep learning, and ML. His passion is using open sourced technologies to create useful toolsets and practical applied solutions, as this was the focus of his master's degree.

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Matt Jackson

Matt Jackson

Matt Jackson is a lead data scientist specializing in graph theory and network analytics. His interest in graphs was sparked during his PhD in systems biology, where network analysis was used to uncover novel features of cell organization. Since then, he has worked in diverse industries - from academia to intelligence, highlighting patterns and risk in complex data by harnessing the latest in graph algorithms and machine learning.

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