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

LP features

Here, we’ll describe the characteristics that can be attached to a pair of nodes and used as predictors for an LP model. We’ll start with topological features, which are built by analyzing both nodes’ neighborhoods. Then, we explore how to use each node’s features and combine them into a feature vector for the pair.

Topological features

Topological features rely on nodes’ neighborhoods and graph topology to infer new links. We can, for instance, use the following:

  • Common neighbors: Given two nodes, A and B, count the number of common neighbors between A and B. This metric assumes that the more common neighbors A and B have, the more likely they are to be connected.
  • Adamic-Adar: A variation of the common neighbors approach, the Adamic-Adar metric incorporates the fact that nodes with fewer connections give more information than nodes with many links. In a web page linking hundreds of other pages, the relevance of each...