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.

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