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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

What are projections?

Data in graph data models usually comprises relationships between things, whether they be people, languages, transport hubs, or any of the other examples we’ve seen throughout the previous chapters. Plenty of data models have several types of nodes and relationships, making one node or edge not equal in meaning or value to another.

These types of graphs are known as heterogeneous graphs, and we have seen them throughout this book. One type of heterogeneous graph is a bipartite graph, where there are two types of nodes. In bipartite graphs, only different types of nodes can share edges. For example, a citation network might be represented as a bipartite graph, with authors connected to articles they have written.

Analysis of heterogeneous graphs with multiple node and edge types can be tricky. To illustrate this, first consider the authorship graph in Figure 8.1. We can answer some simple questions easily, using this graph’s native structure...

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