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Hands-On Graph Analytics with Neo4j

You're reading from  Hands-On Graph Analytics with Neo4j

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Pages 510 pages
Edition 1st Edition
Languages
Author (1):
Estelle Scifo Estelle Scifo
Profile icon Estelle Scifo

Table of Contents (18) Chapters

Preface 1. Section 1: Graph Modeling with Neo4j
2. Graph Databases 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

Summary

This chapter was a long one, as it was our introduction to the GDS plugin. It is important to understand how to define the projected graph and the different entities to be included in it. We will see more examples in the following chapters, as we are going to use this library in all remaining chapters of the book.

The following table summarizes the different algorithms we have studied in this chapter, with some important characteristics to keep in mind:

Algorithm Description Stream/Write Negative weights
shortestPath The shortest path between two nodes using Dijkstra's algorithm Both No
shortestPath.astar The shortest path between two nodes using the A* algorithm and great circle heuristics (requires nodes with latitude and longitude properties) Stream No
kShortestPath The k-shortest paths between two nodes using Yen's algorithm Both Yes
shortestPath.deltaStepping Single source shortest path: the shortest path between a node and all other nodes in the graph...
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