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Graph Data Science with Neo4j

You're reading from  Graph Data Science with Neo4j

Product type Book
Published in Jan 2023
Publisher Packt
ISBN-13 9781804612743
Pages 288 pages
Edition 1st Edition
Languages
Author (1):
Estelle Scifo Estelle Scifo
Profile icon Estelle Scifo

Table of Contents (16) Chapters

Preface 1. Part 1 – Creating Graph Data in Neo4j
2. Chapter 1: Introducing and Installing Neo4j 3. Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph 4. Part 2 – Exploring and Characterizing Graph Data with Neo4j
5. Chapter 3: Characterizing a Graph Dataset 6. Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset 7. Chapter 5: Visualizing Graph Data 8. Part 3 – Making Predictions on a Graph
9. Chapter 6: Building a Machine Learning Model with Graph Features 10. Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning 11. Chapter 8: Building a GDS Pipeline for Node Classification Model Training 12. Chapter 9: Predicting Future Edges 13. Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java 14. Index 15. Other Books You May Enjoy

Importing CSV data into Neo4j with Cypher

The comma-separated values (CSV) file format is the most widely used to share data among data scientists. According to the dataset of Kaggle datasets (https://www.kaggle.com/datasets/morriswongch/kaggle-datasets), this format represents more than 57% of all datasets in this repository, while JSON files account for less than 10%. It is popular for the following reasons:

  • How it resembles the tabular data storage format (relational databases)
  • Its closeness to the machine learning world of vectors and matrices
  • Its readability – you usually just have to read column names to understand what it is about (of course, a more detailed description is required to understand how the data was collected, the unit of physical quantities, and so on) and there are no hidden fields (compared to JSON, where you can only have a key existing from the 1,000th record and later, which is hard to know without a proper description or advanced data...
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