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

Training an inductive embedding algorithm

GraphSAGE is another type of algorithm. Instead of learning the embeddings themselves, which prevents making predictions on new nodes, it learns the function to compute the embeddings, which, once learned, can be applied to unknown nodes. It also has the ability to take into account node properties, making it an interesting algorithm to mix the graph structure and node characteristics into one single vector. In this section, we are going to give some more details about GraphSAGE internals, before using it with our data stored in Neo4j.

Understanding GraphSAGE

GraphSAGE relies on the principle of message propagation in a graph, from one node to its neighbors, and aggregates the received information to iteratively build node representations. It is also known to be scalable due to its neighbor-sampling technique.

Message propagation

Using again the graph represented in Figures 7.1 and 7.2, we are first going to create a one-hot encoding...