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 a transductive graph embedding algorithm

As we stated in the preceding section, a transductive algorithm is characterized by the fact that it works only on a full dataset, meaning it won’t be able to make any predictions on new observations. But, as with the centrality or community detection algorithms we have already crossed in the preceding chapters, these algorithms can be useful in circumstances where your graph is not evolving too fast. The GDS library currently contains two such algorithms: Node2Vec and Fast Random Projection (FastRP). We’ll describe the principles and usage of the Node2Vec algorithm. The usage of the FastRP algorithm will be very similar.

Understanding the Node2Vec algorithm

The Node2Vec algorithm is derived from the DeepWalk algorithm. In order to understand DeepWalk, we also need to know about the Word2Vec and SkipGram models.

As you can imagine, Word2Vec is an embedding algorithm for words within texts. As for a graph, a text...