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

Building and training a pipeline

Similarly to models, in order to add a pipeline to the catalog, we’ll have to train it. Pipeline training requires several steps:

  1. Create and name the pipeline object.
  2. Optionally, compute features from other GDS algorithms (such as graph algorithms, embeddings, or pre-processing).
  3. Define the feature set from the features added in the previous step, and/or any node property included in the projected graph.
  4. Select the ML models to be tested with their hyperparameters: The pipeline training will run all algorithms and select the best one.
  5. Finally, train the model.

The following sub-sections detail each of these steps. The supporting notebook is Pipeline_Train_Predict. This can be found in the Chapter08 folder of the code bundle that comes with this book.

Creating the pipeline and choosing the features

In GDS, we can create three kinds of pipelines:

  • Node classification: Each node gets assigned to one target...