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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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Toc

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

Dynamic graphs

So far in this book, we have studied graphs in a static manner; in other words, we imported graphs from an external data source and the graph content (nodes or relationships) was never changed. Studying the graph structure in this way gives us some information about the data it is representing. However, in a real-life scenario, whether your graph models a road network or an e-commerce website, it will change over time. In a dynamic graph, all parts of the graph can change. Here are some examples of the ways in which a graph can change:

  • Node addition: New users subscribing to a service, new products added to the catalog, or new intersections created.

  • Node removal: Products no longer manufactured or customers leaving.
  • Link addition: An existing customer buys another product, or a road is added between two intersections.
  • Link removal: A closed road or a customer unsubscribing from some services, but maintaining a subscription for other products.

Predicting all of these...

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