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

Implementing the PageRank algorithm

As an example, we will use the PageRank algorithm. It is a centrality metric developed by Larry Page, Google’s co-founder, to rank results on the search engine.

In this section, we will dig into the algorithm’s mechanisms and work on a simple implementation using Python before implementing the algorithm in Java, leveraging the Pregel API.

The PageRank algorithm

This algorithm is based on the following assumptions:

  1. The more connections you have, the more important you are.
  2. Not all connections share the same weight. For example, let’s say a backlink from the New York Times is driving more traffic to your website than a backlink from a less popular website. Scores are propagated from neighbors to account for the neighbor’s importance.
  3. At the same time, links from a website with fewer links show more relevance. Imagine that there’s a Wikipedia article linking every single noun to the corresponding...