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

The GDS pipelines

This section introduces GDS pipelines, where we explain what the purpose of this feature is, illustrate its intended usage, and show the basic usage of the pipeline catalog.

What is a pipeline?

As data scientists, we run data pipelines every day. Any logical flow of action is somehow a pipeline, and when you run your Jupyter notebook, you already have a pipeline. However, here, we refer to explicitly defined workflows, with sequential tasks such as the one we can build with scikit-learn. Let’s take a look at the Pipeline object in this library before focusing on GDS pipelines to understand their similarities and differences.

scikit-learn pipeline

Often, we think about ML as finding the best model for a given problem, but as data professionals, we know that finding the right model is only a small part of the problem. Before we can even think about fitting a model, many preliminary steps are required: from data gathering to feature extraction. Some...