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 learned about the GDS Python client. From graph management (projection, retrieval, and deletion), to running algorithms and retrieving their results in a pandas dataframe, and all we have done in the preceding chapters with Cypher, you are now able to do it without needing to open the Neo4j browser anymore. By only using a Jupyter notebook, you can take advantage of the full power of Neo4j and the GDS. Since the GDS procedures return pandas dataframes, it is quite straightforward to include these results within a Python ML pipeline, for instance, by using scikit-learn, as we have done in the last section of this chapter.

This chapter and the preceding ones have shown you how to extract features from a graph dataset, taking advantage of the graph structure. Features such as a degree, or more generally, centrality metrics, and community ID are only available if you consider the relationships between the entities in your dataset to build a graph. Depending...