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

Exercise

In order to get more proficient with the GDS Python client, I recommend you redo the analysis from Chapter 4, Using Graph Algorithms to Characterize a Graph Dataset, with it. That is, do the following:

  1. Build projected graphs that contain the following:
    1. Person nodes and all relationships.
    2. Person and Movie nodes with all relationships.
    3. Person nodes with KNOWS relationships, undirected.
    4. Person nodes with KNOWS relationships aggregated to keep one single relationship between the same two nodes.
    5. Person nodes with KNOWS relationships aggregated to store the number of relationships between the same two nodes in a weight property.
    6. Using Cypher projection: Movie nodes and a relationship between two movies when at least one person acted in or directed both movies. Save the movie’s release year and the relationship’s weight (the number of persons collaborating on both movies) as node and relationship properties, respectively.
  2. Run graph algorithms: Using the projected...