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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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Estelle Scifo Estelle Scifo
Author Profile Icon Estelle Scifo
Estelle 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

Dijkstra's shortest paths algorithm

Dijkstra's algorithm was developed by the Dutch computer scientist E. W. Dijkstra in the 1950s. Its purpose is to find the shortest path between two nodes in a graph. The first subsection will guide you through how the algorithm works. The second subsection will be dedicated to the use of Dijkstra's algorithm within Neo4j and the GDS plugin.

Understanding the algorithm

Dijkstra's algorithm is probably the most famous path finding algorithm. It is a greedy algorithm that will traverse the graph breadth first (see the following figure), starting from a given node (the start node) and trying to make the optimal choice regarding the shortest path at each step:

Graph traversal (reminder from Chapter 1, Graph Databases)

In order to understand the algorithm, let's run it on a simple graph.

Running Dijkstra's algorithm on a simple graph

As an example, we will use the following undirected weighted graph:

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