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

Automatically Extracting Features with Graph Embeddings for Machine Learning

When dealing with a graph dataset, we can rely on feature engineering and define important features for our context, taking into consideration the graph structure via features extracted from graph algorithms such as node importance or belonging to a community. However, as for other kinds of complex objects—images or texts, for instance—there are ways to automatically extract features from a graph. They are called graph embedding algorithms, and they are able to retain part of the graph structure while representing objects in a low-dimensional space. In this chapter, we will introduce several of these algorithms, which can be used from the Neo4j Graph Data Science (GDS) library: Node2Vec and GraphSAGE. On one hand, Node2Vec is inspired by the Word2Vec text embedding algorithm and only works when the full dataset is known beforehand, meaning we won’t be able to predict embeddings of new...