You're reading from Graph Data Science with Neo4j Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project

Product type Paperback

Published in Jan 2023

Publisher Packt

ISBN-13 9781804612743

Length 288 pages

Edition 1st Edition

Languages

Python

Tools

Neo4j

Concepts

Data Science

Author (1):

Estelle Scifo

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Table of Contents (16) Chapters

Preface

1. Part 1 – Creating Graph Data in Neo4j

2. Chapter 1: Introducing and Installing Neo4j FREE CHAPTER

3. Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph

4. Part 2 – Exploring and Characterizing Graph Data with Neo4j

5. Chapter 3: Characterizing a Graph Dataset

6. Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset

7. Chapter 5: Visualizing Graph Data

8. Part 3 – Making Predictions on a Graph

9. Chapter 6: Building a Machine Learning Model with Graph Features

10. Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning

11. Chapter 8: Building a GDS Pipeline for Node Classification Model Training

12. Chapter 9: Predicting Future Edges

13. Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java

14. Index

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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...

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You're reading from Graph Data Science with Neo4j Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project

Table of Contents (16) Chapters

Using a transductive graph embedding algorithm

Understanding the Node2Vec algorithm

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Authors (2)