This Week in Neo4j: Bloom 2.3, Graph Data Science, Java, AWS, Python, Ontology, Microservices, and More

Graph Data Science features are now available in Bloom 2.3! Just select the Graph Data Science icon and choose from the available algorithms. The GDS plugin needs to be installed on the database for self-managed users, or you can use AuraDS.

To get an idea of what Bloom and Graph Data Science are capable of, read Zach Blumenfeld ’s blog on using graph technology on a freight forwarding logistics network. In this first blog in the series, he gets us started with experimentation and visualization of supply chain data using Neo4j Graph Data Science and Bloom. In his own words: “The data is obscure and unwieldy to deal with in its raw form. It is also heavily anonymized with dates and locations retracted. However, once we get it into Neo4j, you will see that, despite all this, the data almost instantaneously starts to tell a story as insights reveal themselves naturally through the network structure and things become transparent.”

Thank you, Zach!

Yolande Poirier

P.S.: You are invited to attend local GraphSummits in EMEA and APAC. To get a better understanding of what you can expect to achieve at one of these events, read Eva Delier’s blog. After the one-day events, some of the cities – such as Paris, Milan, Berlin, London, Amsterdam, and Tel Aviv – are hosting evening meetups. This is your chance to get together! Find a local event here .

Mehul Gupta is a data scientist and graph enthusiast who enjoys learning about and sharing data-related topics. He blogs on subjects ranging from reinforcement learning and NLP to databases and (of course) graphs. Mehul authored a series about graph analytics in his blog, Data Science in your Pocket. He recently joined the Ninja program as he pursues his graphy passions. Big kudos to Mehul! Follow him on LinkedIn.

JAVA: AWS Lambda in Java With Neo4j
This project by Jennifer Reif is based on the AWS Lambda Java Application and Neo4j’s AuraDB console Java code example. The application connects to Neo4j, runs a query, and returns the results.
GRAPH DB: Why a Graph Database Solves Problems Other Databases Won’t
Jason Koo describes how graph databases are able to surface critical information. He touches on a live use case of graph technology and provides references for further investigation.
GOING META: Ontology-driven Knowledge Graph Construction

Jesús Barrasa creates schemata from the domains of named entities and asks what happens when you generate a #dataimporter model from the Person type in He also shared the code on GitHub, so try it yourself.

APPLICATIONS: Neo4j Data Access For Your .NET Core C# Microservice

Chintan Desai uses the Neo4j C# .NET driver and develops the data access layer for a .NET Core application. He establishes a database session, implements data access, and returns Cypher-generated map projections.

GRAPH DATA SCIENCE FOR SUPPLY CHAINS: Part 1, Getting Started With Neo4j GDS and Bloom

Zach Blumenfeld kicks offf his blog series on Neo4j and graph data science by using Neo4j Graph Data Science and Bloom for experimentation and visualization of supply chain data.

TWEET OF THE WEEK: @coding_charles
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… Of Special Interest

    • This article outlines how researchers can apply algorithms from the Neo4j Graph Data Science (GDS) library to Chainverse data and iterate on their analysis. Read more!
    • Ken Wagatsuma launched his new website: Gnutella, a P2P Network Data Visualized. Check it out!.
    • Rik Van Bruggen wrote this great article about Shakespeare’s plays to understand player-to-player networks, etc. Worth the read!