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

Introducing the LP problem

Let’s pause for a minute and understand what exactly LP is and how we can formulate this kind of problem using machine learning (ML) vocabulary.

LP examples

In order to understand what LP is, let’s see some real-life scenarios where these problems can be and are used:

  • Social networks: In a social network containing people who have certain relationships with each other, we can try and predict who the next people to meet or collaborate on a project will be. We can think of the following types of relationships, but there are many more:
    • Social media (know, follow)
    • Communication network (phone call)
    • Research paper authors: co-authorship of a research paper (research collaboration)
  • Criminal networks: A criminal network, by nature, is not fully known to the people analyzing it (police authorities). The LP technique helps in identifying unknown links between people and better predicting criminal behavior.
  • Entity resolution: Sometimes...