How to Use Hume Node Grouping (Hume Features Video #1)

Check out this first video in our Hume Platform (Neo4j-powered graph insights engine) capabilities exploration where we show Node Grouping within Hume to learn new things from large amounts of data using Graph-powered insight.

Join Graphable‘s VP of Consulting Will Evans for this short video (1:24) in our latest series, and don’t forget to like, comment and subscribe to the Graphable YouTube channel if you have not yet.

Video transcript:

Welcome to the first edition of Hume Features, our new series of videos focused around the most valuable capabilities of the graph-aware Hume platform. Today, we will be exploring node grouping, which enables you to easily and powerfully explore graphs of massive size without running into the typical issues of node sprawl.

Let’s start with one of our more prolific users, Demo User 1. We can see from our exploration tools that this user has reviewed more than 2,000 beers. So let’s try to understand the styles that they enjoy. We might start by expanding the reviews they’ve written then get the beers those reviews are about; and finally, the styles those beers are in.

However, we’ve immediately run into a challenge, as we have a huge amount of data on the screen, and it’s hard to understand any pattern in the styles of beer Demo User 1 enjoys. We’ll set up grouping on the review nodes and try this again. First, we create a simple Computed Attribute, and then we simply enable grouping on the review node and set it to group on this attribute.

Now as we go back through our exploration of Demo User 1’s preferences, we can immediately infer overall patterns in their behavior without losing any detail to drill into the beers they love or the reviews of those beers. The result? A huge beer graph.

We hope you’ve enjoyed this short video, and please reach out to us to learn more about Hume.