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

Running GDS algorithms from Python and extracting data in a dataframe

In a preceding chapter, we learned that GDS algorithms offer multiple run modes, depending on where we want the results to be saved. In stream mode, the algorithm results are just streamed to the user, who has to decide what to do with them. In write mode, the results are persisted in the Neo4j database. Finally, mutate mode will update the in-memory projected graph with the results, which will be lost when the Neo4j instance is restarted, just like all the projected graphs. In this section, we will look at write and stream modes.

The code for the next paragraph is available in the Running_Algorithms_From_Python notebook.

write mode

As we just mentioned, when calling a GDS algorithm in write mode, the results of the algorithm computation will be written back to the main Neo4j graph. This is the only way to persist a result when the Neo4j server is restarted. The result can be either of the following:

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