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 an LP pipeline with the GDS

Our task will be to predict the future collaboration of actors and directors, using the homogeneous graph made of Person nodes and KNOWS relationships. We will only use the persons in the main component according to the connected component algorithm, identified by the MainComponent label.

Creating and configuring the pipeline

The process of creating, training, and making predictions with a GDS pipeline is very similar to the node classification case. We will detail the steps in the following subsections.

Building the projected graph

First, we are going to create a projected graph, as follows:

projected_graph_object = create_projected_graph(
    gds,
    graph_name="graph-lp-collab",
    node_spec={
        "Person": {
            "label": &quot...