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

Computing a node’s degree with GDS

We have studied the node degree metric and its distribution in the preceding chapter, Chapter 3, Characterizing a Graph Dataset. At that time, we computed the node’s degree using a Cypher query. GDS provides a procedure to perform the same computation, on a projected graph. We are now going to use this procedure, whose results are well known, in order to understand the different algorithm modes and configuration options.

All algorithm procedures from GDS use the same syntax:

gds.<algoName>.<executionMode>(<graphName>, <algoConfiguration>)

Here, the following applies:

  • algoName is the name of the algorithm. Note that some algorithms are included in an alpha or beta version, in which case they are accessible via gds.alpha.<algoName> or gds.beta.<algoName>.
  • executionMode is one of stream, write, mutate, estimate or stats, as defined in the GDS project workflow section.
  • graphName...