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

Further reading

If you want to learn more about graph embeddings, I recommend the following readings:

  • Graph Machine Learning by C. Stamile, A. Marzullo,and E. Deusebio, Packt Publishing. It’s a comprehensive introduction to Graph ML. Both supervised and unsupervised algorithms are covered, with applications in various fields including natural language processing (NLP), using networkx and Python ML libraries such as tensorflow. It is a nice complement to this book. As an exercise, you can try to redo the analyses presented in the GML book using the tools we are discussing in this book, Neo4j and GDS.
  • P-GNN algorithm—I talked about this in the Positional or structural section: https://snap.stanford.edu/pgnn/
  • I presented some graph embedding algorithms in this Medium story: https://medium.com/@st3llasia/graph-embedding-techniques-7d5386c88c5
  • The original DeepWalk paper: https://arxiv.org/abs/1403.6652
  • The original Node2Vec paper: https://arxiv.org...