Graphistania 2.0, Episode 15: The Summer Session with Emil

Well this makes me very happy: just before many of us are taking some summer vacations, and ON THE DAY OF MY 2ND VACCINATION SHOT, I am able to publish another Graphistania podcast episode - interviewing my friend and boss (how awesome is it to be able to say that!) Emil Eifrem. We talk about the world, the graph database market, Neo4j the company, and of course, the products. It was a ton of fun, and I even got Emil to agree to publishing the video recording too :) ... Hope you enjoy it as much as I did. Here goes:

Graphistania · Graphistania 2.0 - Episode 15

And of course the video:

Here's the transcript of our conversation:

RVB 00:00:01.709 So have I got your consent to record, please?

EE 00:00:07.850 Fine. [laughter] I want to put it on the record that you have my consent to record this and release the audio.

Contact tracing guide for the Neo4j Browser

Based on the past two blogposts on (Covid-19) contact tracing (see here for the posts, here for the movies), I thought it would be a good idea to pick up an old skill - to create a Browser Guide for Neo4j for people to look at this dataset example more easily. I did this a long time ago for my beergraph as well, so why not do it for the contacttracinggraph :) ...

About the Neo4j Browser and Browser guides

Here's what this is: with Neo4j, the native graph database, we always ship a default user interface called the "Neo4j Browser". It's a interactive application that communicates with the database, and that essentially allows you to fire of Cypher queries and look at / manipulate the contents of your database. Read up about it over here. Once you have done that you will realise that the Browser is actually more than that: it's also a great way for people to learn more about Neo4j, and has a built in mechanism to share "guides" to various topics. If you experiment a bit with the following commands:

Title	Description	Command
Intro	A guided tour of Neo4j Browser	:play intro
Concepts	Graph database basics	:play concepts
Cypher	Neo4j’s graph query language introduction	:play cypher
The Movie Graph	A mini graph model of connections between actors and movies	:play movie graph
The Northwind Database	A classic use case of RDBMS to graph with import instructions and queries	:play northwind graph

you will get to see a number of topics that allow you to familiarise yourself with it really easily. Most of these guides are either built in or available for serving from a webserver. But: you can also develop these guides yourself. There's a really nice worked example over here, but the process really is dead simple:

(Covid-19) Contact tracing follow-up - demo movies

In my previous post I outlined the 4 different blogposts that I wrote about using the Neo4j Graph Database for Contact Tracing. Each of these posts is actually interesting in and of its own, and actually makes for a really nice demo of the capabilities in Neo4j. So I created those today - and put them on a Youtube playlist for you:

(Covid-19) Contact tracing - an amazing graph problem & rabbit hole

In the past couple of days, I have been working with several of my colleagues on a number of projects, all around the world, that are preparing our societies for a post-lockdown strategy that will allow us to keep the Covid-19 pandemic under control, and still regain some of our freedoms. This will be tricky, for sure, but as in so many problems, technology can probably assist.

That's why I started experimenting with how a graph database like Neo4j could help with this. Some of the tracing problems that we will face, are uniquely well suited for a graph database approach: it allows for us to see and understand the indirect contacts that healthy and sick people may have had with one another, and the effects that this could cause in our environments. It also allows for some unique predictive analytics: the structure of our contacts, the network/graph that it constructs, actually says a lot about the importance that parts of the network may play in the evolution of the pandemic. Graph Data Science can give us pointers as to where this should direct our policies.

This has ended up being quite an extensive piece of work. In order to keep it readable, I have cut it up into 4 blogposts, which I will put up all at the same time:

• Part 1: how I go about creating a synthetic dataset, and import that into Neo4j
• Part 2: how I can start running some interesting queries on the dataset, making me understand some of the interesting data points in there and questions that one might ask
• Part 3: how I can use graph data science on this dataset, and understand some of the predictive metrics like pagerank, betweenness and use community detection to direct policies
• Part 4: a number of loose ends that I touched on during my exploration - but surely did not exhaust. 

There's so much potential in this dataset, and in this problem domain in general. I feel like I have gone into the rabbit hole and have just resurfaced for some air. But who knows, maybe I will dive back in and do some more digging - after all, this is interesting stuff, and I love working on interesting topics.

Hope this is as interesting for you as it was for me.

All the best

Rik

Note that these demos will require the following environment: 

• Neo4j Desktop 1.2.7, Neo4j Enteprise 3.5.17, apoc 3.5.0.9, gds 1.1.0, or
• Neo4j Desktop 1.2.7, Neo4j Enterprise 4.0.3, apoc 4.0.0.6 (NOT later! a bug in apoc.coll.max/apoc.coll.min needs to be resolved)