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

Practice with the Pregel API and write an algorithm. If you need some intermediate steps, here are a couple of exercises to help you get started:

  1. Update the Python implementation so that it computes the normalized PageRank given by the following formula:

Here, N is the total number of nodes in the graph.

Warning: Be careful with the score initialization.

  1. Again, using the Python implementation, take into account relationship weights. Hint: Relationship weights are entered into the outgoing degree part of the formula.
  2. Update the Java implementation to track the PR values at each step. We want to be able to see the evolution of PR at each iteration when calling the algorithm in Cypher.

This means adding a new field to our schema of the double[] type and extending it at each iteration.