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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

CSV files with headers

However, in most cases, you will have a CSV file with named columns. In that case, it is much more convenient to use a column header as a reference instead of numbers. This is possible with Cypher by specifying the WITH HEADERS option in the LOAD CSV query:

LOAD CSV WITH HEADERS FROM '<path/to/file.csv>' AS row
CREATE (:Node {name: row.name})

Let's practice with an example. The usa_state_neighbors_edges.csv CSV file has the following structure:

code;neighbor_code
NE;SD
NE;WY
NM;TX
...

This can be explained as follows:

  • code is the two-letter state identifier (for example, CO for Colorado).
  • neighbor_code is the two-letter identifier of a state sharing a border with the current state.

Our goal is to create a graph where each state is a node, and we create a relationship between two states if they share a common border.

So, let's get started:

  • Fields in this CSV file are delimited with semi-colons, ;, so we have to use the FIELDTERMINATOR...
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