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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

Spanning trees

A spanning tree is built from the original graph such that: 

  • The nodes in the spanning tree are the same as in the original graph.
  • The spanning tree edges are chosen in the original graph such that all nodes in the spanning tree are connected, without creating loops.

The next figure illustrates some spanning trees for the graph we have studied in this chapter:

Among all possible spanning trees, the minimal spanning tree is the spanning tree with the lowest sum of weight for all edges. In the preceding diagram, the bottom-left spanning tree has a total sum of weights of 89 (10+33+6+40), while the bottom-right spanning tree has a total sum of weights of 64 (10+20+28+6). The bottom-right spanning tree is hence more likely to be the minimal spanning tree. To verify this, in the following section, we will discuss the algorithm implemented in the GDS plugin to find spanning trees, Prim's algorithm.

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