Executives of Belgian Public Companies - revisited!

Long time ago, when dinosaurs roamed the earth and Neo4j was just a tiny cute little junior graph database ;-),  I wrote a 2 part blogpost about a newspaper article that I had come across in De Tijd about the network of executives of Belgian public companies. You can find the articles over here: Part 1 and Part 2. Turns out - and I really was not aware of this until recently - that the newspaper has been running this type of publication on a yearly basis. Here's another article from 2018 on De Tijd’s website.

So imagine my surprise a few weeks ago, when I was contacted by one of the authors of that article, Thomas Roelens, to verify some info for the 2020 edition of this analysis. We had a great chat, and Thomas basically asked me to double check some of the analysis that he had done himself already. So, contrary to what happened in 2017 (where I had to dig into the HTML source to download the info from the website - Thomas just sent it to me, and basically allowed me to take it for a spin :) ...

Meanwhile, Thomas' article has been published in the newspaper: you can find it over here or over here. But here's my update below too.

What VAT Fraud Detection and Contact Tracing have in common

In the previous blogpost we already illustrated in some detail that the contact tracing graph that we built, has a lot of similarities with a product recommendation system graph. We focused on a the Person-Visit-Place triangle that we had built in our Contact Tracing Graph data model, and converted the red and yellow bits into a Person-Purchase-Product triangles.

There is of course another part to the contact tracing graph that is also very interesting: the Person-Meets-Person subgraph. We derived that graph from the original contact tracing graph, by assuming that if a Person had visited a Place at the same time as another person, they would have been likely to have had a meeting there. This Person-Meets-Person subgraph was the basis for most of our graph analytics.

What Recommender Systems and Contact Tracing have in common

With the Covid-19 pandemic raging in the past few months, I have had a lot of interesting conversations about the use of graph technology and how it could help the world be a better, safer, healthier place. At Neo4j, we even put in place a specific Graphs4Good program, helping out where we can. There's splendid research going on at Covidgraph.org, companies like Elsevier chipping in (and using Neo4j) as well, and I have tried to write up my humble thoughts on how Contact Tracing could really benefit from using graphs as well. See some of my recent posts published on this blog.

Looking at that work, however, I always had a the feeling that I was looking at an excellent example of something else: an excellent example of a great "graph problem". The contact tracing example is a great fit for a tool like Neo4j, and the reason why that is the case is basically because the problem that we are trying to solve with contact tracing (understanding the pandemic spread in our societies, predicting potential evolutions of the pandemic based on contacts between healthy and sick individuals, protecting the healthcare systems by managing the rate of spreading this way) is very much suited for analysis with graph technology. It is a domain where the links between people, the links between people and places, their visits, their meetings are the main important data entities that we need to look at. It's the connections that matter. It's the connections that are becoming the "equal citizens" in the dataset - and therefore we need to spend time and resources analysing it.

But of course I know one thing for sure: there are plenty of other cases that are like that, that are true "graph problems" and that could really benefit from a graph approach to solving it. We know that from all the Neo4j project that we have been running for years. So how do I demonstrate that? How do I show that Contact Tracing is essentially the same thing like a recommendation engine? Or another graph application that we have come to know and love. Let's explore that.

Creating a Contact Tracing Testbed with Neo4j and Faker

Over the past few weeks and months, I have been living through the Covid-19 pandemic like many others. It's not been easy - but at the same time I feel very fortunate to have been able to stay healthy, active, working, and connected. There's a lot of people out there that are a lot less fortunate, and my heart goes out to them.

On this blog, I have been writing about using graphs for Contact Tracing quite a bit. See 

• the original blogpost series over here,
• the demo movies that I made
• the Neo4j Browser guide that you can use

Fortunately, these articles were very well received by the community - we have had a ton of discussions with a variety of different individuals, companies and governments about how to use this technology to prevent that the next lockdown would again require immobilising so many healthy people. If the pandemic's second wave hits, we all want people at risk / sick people to be separated from the healthy population, and manage the spread of the disease in this way. But all of that requires contact tracing to be effective and operational - which is not a trivial thing to do.

This is why I have been looking at creating a very easy to use testbed for Contact Tracing in Neo4j. I wanted to make it super easy for people to create synthetic contact tracing datasets, and then work with them to gain experience - valuable experience for the "real deal" when we have to manage that. That's what this post is about.