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

Characterizing a Graph Dataset

Two graphs can differ in many ways, depending on their number of nodes or types of edges, for instance. But many more metrics exist to characterize them so that we can get an idea of the graph based on some numbers. Just as the mean value and standard deviation help in comprehending a numeric variable distribution, graph metrics help in understanding the graph topology: is it a highly connected graph? Are there isolated nodes?

In this chapter, we are going to learn about a few metrics for characterizing a graph. Focusing on the degree and degree distribution, this will be an opportunity for us to draw our first plot using the NeoDash graph application. We will also use the Neo4j Python driver to extract data from Neo4j into a DataFrame and perform some basic analysis of this data.

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

  • Characterizing a graph from its node and edge properties
  • Computing the graph degree...