Monthly Archives: March 2012

Slides from Chicago Graph Database March Meet-up


View more PowerPoint from Max De Marzi

Thank you very much to Groupon Engineering! They hosted our Graph Database Meet-up at their Headquarters.

Join us April 30th, 2012 for Neo4j Basics and an introduction to Gremlin.

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Visualizing a set of Hiveplots with Neo4j

What should a graph look like and how can I tell two graphs apart?

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MadCoderTV is live on Roku

I’ve had a Roku streaming player for my TV for a few years now and a few months ago I got interested in how it actually worked. I started seeing more channels pop-up and I thought how hard could it be to put one up? So I found their SDK, grabbed their sample application and after a few tweaks, some nice artwork and finding content, it was approved.

Why go through the trouble of doing this to watch videos on my TV when I can just watch them on the laptop? One word… Distraction.

If a video is more than 3 to 5 minutes long, there is a good chance I won’t make it all the way through. It’s hard to sit still when you have the full power of your laptop and the internet at your fingertips. On the TV, as long as there are no commercials, I’m pretty much going to just sit there and watch. Maybe it is some mild form of ADD, maybe it’s normal.
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JUNG in Neo4j – Part 2

A few weeks ago I showed you how to visualize a graph using the chord flare visualization and how to visualize a network using a force directed graph visualization from D3.js.

On Twitter Claire Willett from Riparian Data asked:!/RiparianData/status/169099913580396544

This post on Graphs Beyond the Hairball by Robert Kosara explains why some non-traditional graph visualizations may work better and links us to an article explaining what a Node Quilt is and how it’s useful. We’re going to just take the first step and build a Matrix representation of a graph. We will use one of the JUNG clustering algorithms to help us understand it.
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JUNG in Neo4j – Part 1

It’s nice to have an arsenal. In the world of graph databases, one such stock room is the Java Universal Network/Graph Framework(JUNG) which contains a cache of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.).

We can use JUNG via the Blueprints ouplementation and access it via Gremlin. It doesn’t come pre-packaged with Neo4j, but Michael Hunger playing the role of “Tank” and was able to load up our stock room with a few key strokes.
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Connections in Time

Some relationships change over time. Think about your friends from high school, college, work, the city you used to live in, the ones that liked you ex- better, etc. When exploring a social network it is important that we understand not only the strength of the relationship now, but over time. We can use communication between people as a measure.

I ran into a visualization that explored how multiple parties where connected by communications in multiple projects. We’re going to reuse it to explore how multiple people interact with each other. So let’s make a network of 50 friends and connect them to each other multiple times. Think of it as people writing on your facebook wall.
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