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

Using features from graph algorithms in a scikit-learn pipeline

Now we have all the necessary knowledge to actually use graphs for ML. In this section, we are going to wrap everything up using the GDS Python client to create features and extract data into a dataframe that can be fed into a scikit-learn model training pipeline.

But before we get to this, let me give you an overview of the ML possibilities with graphs.

Machine learning tasks with graphs

In general, ML comprises several types of tasks on various kinds of objects: from sales predictions with time series analysis to patient diagnosis thanks to medical imagery to text translation in many languages with natural language processing (NLP), ML has proven its usefulness in many situations.

In each of these cases, you have to build a dataset made of observations (usually, the rows). Each observation has a certain number of characteristics or features (that is, the columns of your dataset). Depending on the task, you...