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, we’ve explored a few helpful tools for graph data visualization. First, networkx has helped us visualize relatively small graphs in a Jupyter notebook. We have explored the challenge of graph data visualization and learned about graph layout. In the second part, we have used another great tool—part of the Neo4j ecosystem—called Neo4j Bloom. It has many features allowing to deal with graph data stored in Neo4j without writing any Cypher query. We have focused on how to customize the appearance of the graph, choosing the node’s color and size.

Finally, we have discovered a very powerful tool we have to know about when dealing with GDS: Gephi. Here, again, we have focused on node appearance configuration.

In all cases, you are highly encouraged to dig deeper into these tools by yourself, using your own data and/or exploring the features we can’t talk about in this book (unless we double its length, but then nobody would...