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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

Single Source Shortest Path (SSSP)

The aim of the SSSP algorithm is to find the shortest path between a given node and all other nodes in the graph. It is also based on Dijkstra's algorithm, but enables parallel execution by packaging nodes into buckets and processing each bucket individually. Parallelism is governed by the bucket size, itself determined by the delta parameter. When setting delta=1, SSSP is exactly equivalent to using Dijkstra's algorithm, meaning no parallelism is used. A value of delta that is too high (greater than the sum of all edge weights) would place all nodes in the same bucket, hence canceling the effect of parallelism.

The procedure in the GDS library is called deltaStepping. Its signature is as expected:

CALL gds.shortestPath.deltaStepping.stream(graphName::STRING, configuration::MAP)

Its configuration, however, is slightly different:

  • startNode: The node from which all shortest paths will be computed
  • relationshipWeightProperty: The usual relationship...
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