This Week in Neo4j – example in Go, Graph Neural Network to approximate Network Centralities, Zoom Calls Graph

Hi everyone,

We’re just started Lockdown #2 in London and Rik Van Bruggen has a timely blog post exploring the 100s of Zoom calls that he’s been in since Lockdown #1 back in March.

Florent starts building a example in Go, Kristof Neys approximates network centralities using Graph Neural Networks, and David Allen explains how queries work.

And finally, NODES 2020 Extended is a series of talks that we couldn’t quite fit into the NODES 2020 schedule. This week we had the first of those, featuring talks about Contentful and Enterprise Application Integration with graphs.

Cheers, Mark, Karin, and the Developer Relations team

This week’s featured community member is Liz Maida.

Liz Maida - This Week’s Featured Community Member

Liz Maida – This Week’s Featured Community Member

Liz Maida is the Founder and CEO of Uplevel Security, a security automation company that uses graphs for real-time alert correlation. She previously worked at Akamai Technologies where he played a lead role in Akamai’s initial efforts in DDoS mitigation, fraud detection, and mobile authentication.

Liz is a long time user of Neo4j, using it at Uplevel, along with machine learning approaches, to improve the effectiveness of cypher security teams. She recently presented Which Comes First – The Data Model or the Algorithm? at the NODES 2020 conference, in which she explains some of the real world challenges of graph data modelling.

In the talk, Liz gives us some background of the security domain, explains what data does and doesn’t go into the graph, and explains how the model evolved over time. She also shows how to identity connected components and predict malicious nodes.

Building a example in Go with Florent Biville

Our video this week is from a new live stream being hosted by Florent Biville, Developer Experience Engineer for Drivers at Neo4j.

In this session, Florent starts building the Medium clone using Neo4j and the Go driver.

A Graph Neural Network to approximate Network Centralities in Neo4j

Kristof Neys is currently doing an internship at Neo4j, where he’s exploring how Graph Neural Networks can be used with Neo4j.

In his first blog post he explores how a Graph Neural Network can be deployed to approximate network centrality measures, such as Harmonic centrality and Eigenvector centrality, before storing the results back into Neo4j.

How Queries Work in Neo4j

Neo4j users often ask “what’s going on with my query” and in David Allen’s latest blog post he answers this question.

In the post David explains the role of transactions, how their lifecycle works, and how transactions relate to queries. He also describes the procedures we can use to understand what’s going on and explains how clusters come into all this.

New Udemy course, Neo4j Commander release, NODES 2020 Extended

Making sense of 2020’s mad calendar with Neo4j

Rik Van Bruggen’s latest blog post is a really apt one for the Coronavirus enforced year of video calls.

Rik shows how to get his Google Calendar meetings into Neo4j, and then runs Cypher queries to discover who he meets with most and his worst day for meetings. He definitely has a lot more meetings than I do!

Tweet of the Week

My favourite tweet this week was by Matt Redlon:

Don’t forget to RT if you liked it too!