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

Predicting Future Edges

Link prediction (LP) is a key topic in Graph Data Science (GDS), since it is a problem very specific to graphs. While we can do classification for many kinds of datasets, not only graphs, LP can only be performed if we have links, meaning if our data is a graph. But the applications of these problems are quite wide: from understanding the dynamics of social network to product recommendations to criminal network analysis.

This chapter is going to give you a short introduction to the LP problem. We will define what observations are and how to build the initial dataset. We will also talk about the metrics that can be used to infer the presence of a hidden or future link and compute them using the GDS library. Finally, we will use a GDS pipeline to build a simple link prediction model, fit it on data stored in Neo4j, and make predictions.

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

  • Introducing the LP problem
  • LP features...