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

Understanding a graph’s structure by looking for communities

In a graph, the repartition of edges is often a key characteristic. Indeed, graph traversal is used by many algorithms to propagate some values from one node to its neighbors, until some equilibrium is reached. Knowing in advance that some groups of nodes are totally isolated from, or share very few links with, the rest of the graph is key information to understand the result of such algorithms. Besides those technical details, the knowledge that some nodes tend to be more connected with each other with respect to other nodes in the graph, forming a community can also be used as an input feature for an ML model. You can, for instance, imagine finding communities in your user base depending on the products they frequently buy and identifying the group of coffee aficionados, different from the group of tea lovers, that will get different recommendations.

Number of components

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