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

Using Graph Algorithms to Characterize a Graph Dataset

So far, you have been able to distinguish between different types and topologies of graphs using simple observations and metrics, such as degree distribution. But we can extract more information from a graph structure. In this chapter, we will learn how to find clusters of nodes—or communities—in a network, only based on the nodes and edges in a graph. We will also learn about node importance algorithms, such as PageRank. To do so, we will install and learn the principles of the Neo4j Graph Data Science (GDS) library, which allows us to run both unsupervised and supervised graph algorithms.

This chapter is a key chapter since lots of the concepts explored herein will be used in the rest of the book, so you are encouraged to stay focused until the end.

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

  • Digging into the Neo4j GDS library
  • Projecting a graph for use by GDS
  • Computing...