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

Visualizing a small graph with networkx and matplotlib

When the graph is small enough, such as the ones represented in the previous screenshots (Figure 5.2 and 5.3), it can be convenient to visualize them using the matplotlib plotting library. In this section, we are going to reproduce the visualizations displayed previously.

When dealing with graphs in Python, fortunately, we do not have to create our own data structure and implement our algorithms. As with many other tasks, we can just pip install a package developed by the fantastic open source community around Python. For graphs, the most used package is called networkx. Let’s go ahead and go through our next Jupyter notebook.

Visualizing a graph with known coordinates

In this section, we are going to draw a graph representing a part of the road network around the Colosseum in Rome. This data was extracted using the osmnx package, but we are not going to detail its extraction process here, even if osmnx makes it...