Using Neo4j Bloom for fraud detection, discovery and visualization

Over the past couple of weeks, I have been discussing and showing Neo4j's new Bloom graph discovery and visualization product to everyone that would have a moment to spare. It's soooo much fun to show a tool you love, and Bloom is definitely one of those. And I have also recorded some odf these demo-sessions - you can find part 1, part 2 and part 3 of these recordings on this blog. All of these recordings use my (in)famous Belgian Beergraph dataset - and that's all good fun...

But of course, exploring a beergraph is not really a "business-y" use case. So I decided I would record a Bloom demo using a realistic dataset that centers around using Neo4j for Fraud Detection purposes. You will find all of the important concepts of the Beergraph demos here as well:

• navigating the graph using graph patterns
• using nifty selection / deselection techniques to only show what you need in the graph
• creating better visualizations with colours and icons
• editing the graph straight from the Bloom interface
• creating custom search phrases for business uses and giving them near-natural language graph search capabilities.

Here's the recording:

Graphs are blooming - again!

A few weeks ago, I wrote a two part blog post about Neo4j Bloom and how I was playing around with my BeerGraph and figuring out some cool features of the new Neo4j product. You can find these posts over here

• part 1
• part 2

It included a first little demo video,

that seems to have been liked by a bunch of people :) ... thanks for that.

Part 2/2: Graphs are Bloom-ing

Earlier I wrote about how I connected the newly announced preview version of Neo4j Bloom to my good old faithful Belgian BeerGraph. See part 1 of this 2-part series for that story. I actually split up the story into two parts, because I feel like there's a super interesting and powerful part to Bloom that deserves a bit more attention: the mechanism of the custom Search Phrases.

As we mentioned in the previous post, Bloom structures your exploration and discovery into specific "views" on the graph data, called "Perspectives. You can select the perspective you find most appropriate from a dropdown - and customize/tweak/create perspectives yourself if you are not happy with the auto-generated starting point.

Part 1/2: Graphs are Bloom-ing

Last week something happened that really excited me. We, Neo4j, finally announced Bloom and demonstrated our own Graph Visualisation and Discovery tool, Neo4j Bloom. This is a technology that we have long been pondering, have experimented with in a number of ways, and have long looked to find and develop an offering that would be interesting and differentiated in what is already a very well looked-after marketplace. 

I am not exaggerating when I say that is truly exciting. Not only do many of our customers want to be able to visualise the results of their graph queries, but the graph data model is also unique in the way that it provides such an intuitive, easy to understand data model that lends itself so well to a GRAPH-ical representation. It truly fits into the Graph Platform vision that Neo4j has been advocating since 2017.