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

In this chapter, you have learned about the LP problem, an ML technique that’s only possible with graph data. It can be used in many contexts to predict future or unknown links between any type of nodes, as long as we have some example or context data. You have learned how to build an LP pipeline with Neo4j’s GDS, which takes care of negative observation sampling, model training, and storage for us.

This chapter is the last one where we will talk about predictions and ML. Overall, we have studied several use cases for ML on graphs, including node classification and future/unknown LP. You have learned how to extract graph-based features or embeddings to feed an ML model in your preferred library (we’ve used scikit-learn). You have also learned that the whole ML pipeline can be managed within Neo4j and its GDS library thanks to built-in pipelines and models.

GDS contains many interesting tools, but it is generally still young compared to other ML tools...