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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

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781804618035
Length 236 pages
Edition 1st Edition
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Authors (2):
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Gary Hutson Gary Hutson
Author Profile Icon Gary Hutson
Gary Hutson
Matt Jackson Matt Jackson
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Matt Jackson
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Table of Contents (16) Chapters Close

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 15. Other Books You May Enjoy

Building a Knowledge Graph

This chapter will extend your knowledge further and introduce knowledge graphs. While learning what a knowledge graph is, you will also get hands-on practice with cleaning data in preparation for ingesting into a graph. This will teach you about the hidden side of data science and graph modeling, in which you spend much of your time cleaning data and getting it ready to commence modeling.

Moreover, we will teach you the best methods of ingesting your data into a graph. After that, you will be ready to analyze your knowledge graph, which will be further extended by finding communities in your knowledge graph, with a technique known as community detection.

Community detection is commonly used to discover groups or clusters of similar items in your network. These methods can be utilized, for example, to find influential groups posting about a certain narrative on social media, or in the example we are going to utilize, to look for similar literature when...

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