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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
Author Profile Icon Scifo
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

Using A* within the Neo4j GDS plugin

The A* algorithm is accessible through the gds.alpha.shortestPath.astar procedure. The signature follows the same pattern as the other algorithms: the first parameter is the name of the projected graph the algorithm will use, while the second parameter is a map specific to each algorithm. In the A* algorithm configuration, we will find the same startNode, endNode, and relationshipWeightProperty we have already used for the shortestPath procedure. On top of that, two new properties are added to specify the name of the node property holding the latitude and longitude: propertyKeyLat and propertyKeyLon. Here is an example call to the A* algorithm on a projected graph:

MATCH (A:Node {name: "A"})
MATCH (B:Node {name: "B"})
CALL gds.alpha.shortestPath.astar.stream("graph", {
startNode:
A,
endNode: B,
relationshipWeightProperty: "weight",
propertyKeyLat: "latitude",
propertyKeyLon...
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