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

Exercises

Challenge yourself with the following exercises related to the content covered in this chapter:

  1. Can you imagine an example of a tri-partite graph?
  2. Create the RELATED_TO relationship between movies that share at least one person (as actor or director).

Update the Cypher query we used to compute the degree distribution to obtain the normalized degree (divide by the total number of nodes in the graph).

  1. Can you draw the weighted degree distribution (total)?

Hint: The weighted total degree is the sum of all weights of relationships attached to a given node.

  1. Advanced: Can you write a Cypher query to compute the triangle count for each node?

Here is the code to create the small graph we used as an example in Neo4j:

CREATE (A:Label {id: "A"})
CREATE (B:Label {id: "B"})
CREATE (C:Label {id: "C"})
CREATE (D:Label {id: "D"})
CREATE (E:Label {id: "E"})
CREATE (A)-[:REL]->(B)
CREATE...