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

Building a Machine Learning Model with Graph Features

So far, we have explored how to build and understand a graph dataset. We have computed some key metrics, such as the degree distribution. Using unsupervised algorithms, such as community detection, we have also better-identified graph structures. Finally, the graph visualization tools have enabled us to see the content of the dataset, and visually identify some aspects of the graph. Now, it is time to start applying this knowledge to build a machine learning (ML) model. In this chapter, we will introduce the Python client for the Graph Data Science (GDS) library, by allowing it to run graph algorithms directly from Python, without writing any Cypher. After computing and extracting our features from Neo4j, we will build a scikit-learn pipeline to train a model and make predictions.

In this chapter, we’re going to cover the following main topics:

  • Introducing the GDS Python client
  • Running GDS algorithms from Python...