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

What this book covers

Chapter 1, Introducing and Installing Neo4j, introduces the basic principles of graph databases and gives instructions on how to set up Neo4j locally, create your first graph, and write your first Cypher queries.

Chapter 2, Using Existing Data to Build a Knowledge Graph, guides you through loading data into Neo4j from different formats (CSV, JSON, and an HTTP API). This is where you will build the dataset that will be used throughout this book.

Chapter 3, Characterizing a Graph Dataset, introduces some key metrics to differentiate one graph dataset from another.

Chapter 4, Using Graph Algorithms to Characterize a Graph Dataset, goes deeper into understanding a graph dataset by using graph algorithms. This is the chapter where you will start to use the Neo4j GDS plugin.

Chapter 5, Visualizing Graph Data, delves into graph data visualization by drawing nodes and edges, starting from static representations and moving on to dynamic ones.

Chapter 6, Building a Machine Learning Model with Graph Features, talks about machine learning model training using scikit-learn. This is where we will first use the GDS Python client.

Chapter 7, Automating Feature Extraction with Graph Embeddings for Machine Learning, introduces the concept of node embedding, with practical examples using the Neo4j GDS library.

Chapter 8, Building a GDS Pipeline for Node Classification Model Training, introduces the topic of node classification within GDS without involving a third-party tool.

Chapter 9, Predicting Future Edges, gives a short introduction to the topic of link prediction, a graph-specific machine learning task.

Chapter 10, Writing Your Custom Graph Algorithms with the Pregel API in Java, covers the exciting topic of building an extension for the GDS plugin.