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 have learned about graph embeddings, first learning what embedding is and that nodes, edges, and whole graphs can be vectorized independently. Focusing on node embeddings, you have then learned the principles of two algorithms included in the GDS library. Node2Vec, inspired by the world of texts, is a transductive algorithm, while GraphSAGE, a message-passing algorithm, is inductive and is able to predict the embedding of unseen nodes.

You have been able to extract node embeddings for nodes stored in Neo4j using the GDS implementation of these algorithms. In addition, you have discovered the GDS model catalog and been able to train a GraphSAGE model, store it into the GDS in-memory model catalog, and reuse it to predict new node representations.

In the coming chapters, we will use these embedding models and discover a new feature of GDS: pipelines. We will use these pipelines to train a node classification model. In the following chapter, we will...