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 graph embedding algorithms

This section will introduce the principles of graph embeddings and explain the idea behind two of the most famous algorithms: Node2Vec and GraphSAGE. In the following sections, we will use the GDS implementation of these two algorithms to extract embeddings for nodes stored in a Neo4j database.

Defining embeddings

Machine learning (ML) algorithms—classification or regression—require an input matrix made of observations (rows) and features (columns). While this is trivial for tabular datasets (for example, the Iris or Titanic datasets), this is already a challenge for datasets made of more complex objects such as texts, images, or graphs. The question is: how can we build a matrix from these objects while preserving their nature? By nature, we mean here the key characteristics that will not kill the predictive power of our data. In the case of texts, this is the meaning of the sentences or words. We will see later in this chapter...