Book Image

Graph Data Science with Neo4j

By : Estelle Scifo
5 (1)
Book Image

Graph Data Science with Neo4j

5 (1)
By: Estelle Scifo

Overview of this book

Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.
Table of Contents (16 chapters)
1
Part 1 – Creating Graph Data in Neo4j
4
Part 2 – Exploring and Characterizing Graph Data with Neo4j
8
Part 3 – Making Predictions on a Graph

Discovering the Wikidata public knowledge graph

Wikidata is a publicly available knowledge graph. It stores data in the RDF format. Like many RDF-like data stores, the query language used by Wikidata is SPARQL. Even if this is not the main topic of this book, we will see a couple of examples by the end of this chapter so that you can perform basic queries.

Wikidata data can be accessed via the following methods:

  • A web browser, starting from the home page at https://www.wikidata.org/. Then, use the search bar to find the item of interest for you.
  • A SPARQL playground, which is available at https://query.wikidata.org/.
  • A public API using the endpoint: http://query.wikidata.org/sparql?format=json&query=””.

You are highly encouraged to navigate through Wikidata using your browser to understand the data format. You can, for instance, start from here:

  • The Neo4j page at https://wikidata.org/wiki/Q1628290
  • The page for India at https:/...