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

Summary

This chapter taught you some aspects of graph statistics. You now know a few metrics you need to compute when you first start analyzing a new graph, from the number of nodes/edges and the node and edge types to degree-related metrics and distribution.

You also installed the Neo4j Python driver and learned how to extract data from Neo4j to Python and create a DataFrame from data exported from Neo4j.

In the next chapter, we will dig deeper into graph analytics by using unsupervised graph algorithms to learn even more about graph topology. We will learn how to find clusters or communities of nodes in the graph. On the way, we will install and learn about the basic principles of the Neo4j Graph Data Science Library, the plugin we will use intensively in the rest of this book.