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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

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Length 510 pages
Edition 1st Edition
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Author (1):
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 Scifo Scifo
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Scifo
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Table of Contents (18) Chapters Close

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

Connected versus disconnected

Last but not least is the notion of connectivity or a connected graph. In the next diagram, it is always possible to go from one vertex to another, whatever pair of vertices is considered. This graph is said to be connected. On the other side of the figure, you can see that D is isolated - there is no way to go from D to A, for example. This graph is disconnected, and we can even say that it has two components. We will cover the analysis of this kind of structure in Chapter 7Community Detection and Similarity Measures. The following image shows connected versus disconnected graphs:

This is a non-exhaustive list of graph properties, but they are the main ones we will have to worry about within our graph database adventure. Some of them are important when creating the whiteboard graph model for our data, as we will discuss in the next section. 

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Hands-On Graph Analytics with Neo4j
Published in: Aug 2020
Publisher: Packt
ISBN-13: 9781839212611
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