Graphistania 2.0 - Episode 6 - The One with the CovidGraph

So, when I started working with Graphs in 2012, one of the first community use cases that I encountered was all about biotech. I met a few people from the University of Ghent, who were working on some amazing protein interaction networks - and it was fascinating. Over the years, we have done quite a few activities on this, and we have kind of built a nice life sciences and healthcare community around Neo4j. Some amazing work is being done there.

One of the most amazing cases out there, has been the use case of the German Center for Diabetes Research, who have been scouring the scientific universe for ways of finding cures against diabetes. Look at this brief video or read this article to know more about it:

Why am I telling you this? Well, with the global Covid-19 pandemic sweeping around the globe, and many of us being affected in small or big ways, our Neo4j Graph Community has been doing the most interesting things to try and apply the "power of the graph" to this complex and intricate problem. Take a look at covidgraph.org for their work. When I learned about it, I immediately thought about talking to some of the "chief instigators" and inviting them for a podcast interview - which we made happen at record speed :) ...

So here it is: a chat about Covid-19, and about how graphs will help us make sense of the data. Let's hope it proves to be useful.

Supply Chain Management with graphs: part 3/3 - some SCM analytics

I've been looking forward to writing this: this is the last of 3 blogposts that I have been planning to write for weeks about my experiments with a realistic Supply Chain Management Dataset. There's two posts before this one:

• In the first post I found and wrangled a dataset into my favourite graph database, Neo4j
• In the second post I got acquainted with the dataset in a bit more detail, and I was able to do some initial querying on it to figure out what patterns I might be able to expose.

In this this third and last post I would like to get a bit more analytical with the dataset, and do some more detail investigation in order to better understand some typical SCM questions. Note that I am far from a Supply Chain specialist - I barely understand the domain, and therefore I will probably be asking some silly questions initially. But bear with me - and let's explore and learn, right?

Supply Chain Management with graphs: part 2/3 - some querying

So in the previous post, we got introduced to a dataset that I have been wanting to get into Neo4j for a long time: a Supply Chain Management dataset. Read up about it over here, but the long and short of it is that we got ourselves into the situation where we have an up and running Neo4j database with 38 different multi-echelon supply chains. Result!

As a quick reminder, here's what the data model looked like after the import:

Or visually:

Data validation and profiling

The first thing to do when you have a new shiny dataset like that, is of course to get a bit of a feel for the data. In this case, it really helps to understand the nature of the different SupplyChains - as we know from the original Excel file that they are quite different between the 38 of them. So let's do some profiling:

match (n) return distinct labels(n), count(*)

Supply Chain Management with graphs: part 1/3 - data wrangling and import

Alright, I have been putting the writing of this blogpost off for too long. Finally, on this sunny Saturday afternoon where we are locked inside our homes because of the Covid-19 pandemic, I think I'll try to make a dent in it - I have a lot of stuff to share already.

The basic idea for this (series of) blogpost(s) is pretty simple: graph problems are often characterised by lots of connections between entities, and by queries that touch many (or an unknown quantity) of these entities. One of the prime examples is pathfinding: trying to understand how different entities are connected to one another, understanding the cost or duration of these connections, etc. So pretty quickly, you understand that logistics and supply chain management are great problems to tackle with graphs, if you think about it. Supply Chains are graphs. So why not story and retrieve these chains with a graph database? Seems obvious.

We've also had lots of examples of people trying to solve supply chain management problems  in the past. Take a look at some of these examples:

• https://neo4j.com/graphgist/supply-chain-management
• https://neo4j.com/blog/graph-technology-supply-chain-transparency-corporate-social-responsibility/
• https://neo4j.com/news/graphing-the-supply-chain/ and https://www.enterprisetimes.co.uk/2019/09/10/graphing-the-supply-chain/
• https://neo4j.com/blog/nlp-at-scale-maintenance-supply-chain-management/

And of course some of these presentations from different events that we organised:

• Our friends at Caterpillar used Neo4j for this:
• TransparencyOne actually built a business on it:

So I had long thought that it would be great to have some kind of a demo dataset for this use case. Of course it's not that difficult to create something hypothetical yourself - but it's always more interesting to work with real data - so I started to look around.

Graphistania 2.0 - Episode 5 - This Month in Neo4j

Friends.

These are interesting times. These are difficult times, but we can deal with it together, as a community, as a graph. So that's why we were super happy that, just as Belgium was going into lockdown last week, we were able to record another Graphistania podcast episode for you, talking about the world in general, but also covering some of the amazing graph use cases that drifted over our screens in the past month, in the This Week in Neo4j (TWIN4J) newsletter.

There were actually many things to talk about, in terms of fascinating graph use cases, and I will highlight only the most striking ones here.

Our friends at Kineviz did some really interesting and timely work on  COVID-19 temporal and spatial data visualization. This stuff is really important to understand, as pandemic spreads clearly follow graph patterns. Read Connected if you are not convinced. 

Worth highlighting: Bloodhound: Windows network penetration testing with Neo4j, had a new release that you might want to take a look at. If you are not familiar with Bloodhound yet, you may also want to check out my interview with the Bloodhound crew on this podcast a while back. 

We published this fun little thing called a Neo4j Treasure Map - check it out! 

Finally - we also have a a Winegraph! It's a great example of importing data from the web using Norconex.  

Some interesting stuff on using Neo4j for Gene ID mapping: take a look! 

Another examle of enriching graphs with Wikidata, from the one and only Mark Needham: look at Mark's blog over here! 

Don't forget: we Introduced the Neo4j Graph Data Science plugin with examples from the "Graph Algorithms" book. 

A really interesting tweet about a visualisation of the US Supreme court as a graph db... Would love to see more like that. 

And for some fun: Pokégraph: Gotta Graph 'Em All! 

Some important stuff: we did a great 4.0 webinar that is giving you a lot of info on what to expect in the new version of Neo4j.  

There was a great update to NeoMap: Visualizing shortest paths with neomap ≥ 0.4.0 and the Neo4j Graph Data Science plugin.

Those were the most important ones. So let's talk about these now - I am sure there's a lot of cool stuff here fore everyone!

Graphistania 2.0 - Episode 4 - This Month in Neo4j

Yey! My friend StefanW and I got round to recording another Graphistania episode, episode 4 already - time flies when you are having fun! This month, again, we have so much great content popping up in the This Week in Neo4j (Twin4j) newsletter, that we could probably fill a few hours talking about it. So in the podcast, we will only talk about a handful - covering things like

• the great momentum that we have in the Neo4jcommunity! Take a look at initiatives like Global Graph Celebration Day, and listen to Neo4j's mad scientist Michael Hunger on graphs, databases and relationships on a different podcast.
• the start of the Neo4j Graphtour, for the 3rd year already, in Amsterdam. This year this actually coincided with the launch of Neo4j 4.0 - one of the best releases of Neo4j that I have personally ever witnessed. I have actually been writing a bit about this myself - read about how I how I child-proofed my beergraph with fine-grained security and how I did something similar by adding security to a fraud investigation graph. There's other articles as well, like When and how to implement Sharding in Neo4j 4.0.
• Various personal graphs projects, like Mark's Australian Open graph and the QuickGraph #3 on Itsu Allergens. I love how Mark summarized it: most of analysis could be done in relational, but "Things got more interesting in the last section where we did set analysis. I found having the data in a graph structure made was helpful for answering these questions, especially when we were looking for the non existence of a relationship."
• a number of Health related articles, like that visualisation of the data from the Personal Genome Project, or the Google project wiuth the “Largest Ever” Map of Brain Connectivity
• And then of course there were various "Other" posts that we really liked, like how to be Working With Spatial Data In Neo4j GraphQL In The Cloud, and the post about Aaia - AWS Identity And Access Management Visualizer And Anomaly Finder. Last but not least, you should also take a look at the Graphaware Hume platform - with an excellent powerful demo recording over here.

Experimenting with Conflicting access privileges in Neo4j 4.0

In the past couple of weeks, I have been playing around with the shiny new security features of Neo4j 4.0. They are truly interesting - both for childproofing beergraphs and for ensuring that your sensitive fraud databases are properly secured. Take a look at the previous post, and I think you will understand why.

In this post, I wanted to talk about something that I have seen so many times in my previous lives in the security industry, and that also became evident in my 4.0 research. It's got to do with conflicting security privileges. In a nutshell, this is to do with the case where

• a specific user / role would receive a particular set of privileges from one policy
• the same user / role would receive a different, and contradictory privilege from another policy. 

In that case, we need clear rules to understand what would happen. In the case of Neo4j 4.0, this is reasonably well explained as part of the documentation - see the documentation site on this topic - but in this post I will try to give you a realistic, but simple example.

Creating Conflict

We'll start working on this with the same database as the previous post, the fraud dataset. If you don't have it yet, just download it from this link. Once we have the database up and running as a separate user database, we can switch to the system database and create a separate user for these tests.

//create a separate user for engineering the conflicting privileges
CREATE USER conflicted_user SET PASSWORD "changeme" CHANGE NOT REQUIRED;
CREATE ROLE conflicted_role AS COPY OF reader;