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

Projecting a graph for use by GDS

GDS doesn’t operate directly on the data stored in Neo4j. Tuned for optimal performance, it uses its own data structure, which can be configured to contain a minimal amount of entities to optimize memory. While your Neo4j graph may contain tens of node labels, each with multiple properties, some algorithms will only use a single node label (for example, User) and no property. The GDS library offers the possibility to create a projected graph containing only these nodes. A so-called projected graph can be created using two different procedures:

  • gds.graph.project: For native projection
  • gds.graph.project.cypher: For Cypher projection

We are going to detail both of these procedures in the following sections.

Backward compatibility

If you used GDS prior to its 2.0 version, the aforementioned procedures used to be called gds.graph.create and gds.graph.create.cypher, respectively.

Native projections

In a native projection...