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

Introducing the GDS Python client

The previous chapters introduced the Neo4j GDS library. There, you discovered the concepts of projected graphs and the procedures to run specific graph algorithms from Cypher. If you do not have direct access to Neo4j Browser, or if you want to automate your data processing, it might be convenient to be able to use GDS procedures from Python. One possible approach is to use the Neo4j Python driver introduced in a preceding chapter (Chapter 3, Characterizing a Graph Dataset) and write code like this:

# driver instantiation
from neo4j import GraphDatabase
driver = GraphDatabase.driver(
      "bolt://localhost:7687",
      auth=("neo4j", "<PASSWORD>")
)
with driver.session() as s:
     # create projected graph named 'pG'
     s.run("CALL gds.graph.project('pG', 'NodeB',...