This Week in Neo4j: AuraDS on Vertex AI, Going Meta Series, Cypher Cheatsheet, GraphConnect Recordings, Centrality Algorithms, and More

Back in January, we previewed Neo4j AuraDS and Google Cloud Vertex AI’s partnership and demonstrated how you can build and deploy graph-based machine learning models. AuraDS is graph data science as a service now running as a managed service on top of GCP.

With the Neo4j Graph Data Science platform, you can easily use graph structure to compute algorithms or create embeddings and increase the accuracy and reliability of machine learning pipelines. The worked example from the blog stores data in AuraDS, and computes graph embeddings with FastRP to feed into the Vertex AI workflow.

If you’re interested in getting started on Google Cloud Vertex AI with AuraDS, this blog is for you.

Yolande Poirier

P.S.: We’d like to understand how you, developers, are building GraphQL APIs with managed cloud services. If you have used a GraphQL managed service, we’d love to learn about your experience and how it could be improved in this survey.

Gabriel Tardif has been using Neo4j for the past two years to manage real estate data. He joined the Ninja program and has been helping others as he ramps up his own skills in Cypher and the Neo4j platform. He is now focusing on improving his query tuning, operator, APOC, and algorithm knowledge.

Gabriel is always ready to lend a hand to his fellow nodes. Big kudos to Gabriel! Find him on the community website.

A decent cheatsheet can make life a lot easier. This one is a veritable dictionary that you can bookmark for future reference. All major Cypher commands are helpfully organized into functional categories and are accessible through a menu in the margin.
It’s easy to get started on Google Cloud Vertex AI with Neo4j AuraDS. The example in this blog stores data in AuraDS, and computes graph embeddings with FastRP to feed into the Vertex AI workflow.

GraphConnect was just last week! Now you can watch the recorded keynote presentations. Keep an eye on this playlist – more session recordings will be made available over the coming weeks.

GRAPH ANALYTICS: Determining Important Nodes in a Graph Using Neo4j

In the third blog of his series, Mehul Gupta queries his database with centrality algorithms to rank nodes based on importance.

GOING META: A Series On Graphs, Semantics, and Knowledge

Explore how semantics applies to property graphs in this hands-on series with Jesús Barrasa. Each episode has a wealth of information about Cypher and Sparql, semantic search, SHACL, and much more.

FOR BEGINNERS: Working with Graph Databases and Neo4j
Beren Erchamion takes his first steps with graph databases and sets up Neo4j Community Server on his MacBook Pro.
TWEET OF THE WEEK: @rotnroll666
Don’t forget to retweet if you like it!
… Of Interest

Using Heroku and Neo4j, Tom Nijhof made a Python license finder where you can type in all your Python packages and find out what licenses those packages and their dependencies need. Check out the tool!

Khadijah is a tool to take a Golang struct and write simple CRUD Cypher queries. It works with Neo4j, Redis Graph, and any other DB with Cypher support. Check out the GitHub project.

New release! Neo4j Migrations (Maven Plugin) version 1.7.0 is available on Maven.

The 4.0 release of Graphlytic was just released. It allows you to manage multiple knowledge graphs in one place. Read the blog.