NeoTechnology was featured on TechCrunch after raising a Series B round, and it has an entry on CrunchBase. If you look at CrunchBase closely you’ll notice it’s a graph. Who invested in what, who co-invested, what are the common investment themes between investors, how are companies connected by board members, etc. These are questions we can ask of the graph and are well suited for graph databases.
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Michael Hunger imported the Hubway Challenge dataset into a Neo4j graph database, and made it available for us to play with.
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Social applications and Graph Databases go together like peanut butter and jelly. I’m going to walk you through the steps of building an application that connects to Facebook, pulls your friends and likes data and visualizes it. I plan on making a video of me coding it one line at a time, but for now let’s just focus on the main elements.
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In the late 90s Michael Jordan, Scottie Pippen, and Dennis Rodman of the Chicago Bulls dominated the basketball court. They were the key players of the Chicago Bulls, and dominated the NBA offensively and defensively. When exploring a social network you’ll want to find who the key players are for a variety of reasons:
Disrupt: Who should be removed from the network to disrupt it?
Protect: Who should be protected in order to keep the network functioning?
Influence: Who should be influenced in order to change social opinion?
Learn: Who should be questioned in order to know what is going on?
Redirect: Who should be moved to alter social flows?
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Last week we saw Sigma.js, and as promised here is a graph visualization with Three.js and Neo4j. Three.js is a lightweight 3D library, written by Mr. Doob and a small army of contributors.
The things you can do with Three.js are amazing, and my little demo here doesn’t give it justice, but nonetheless I’ll show you how to build it.
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I’ve done a few posts recently using D3.js and now I want to show you how to use two other great Javascript libraries to visualize your graphs. We’ll start with Sigma.js and soon I’ll do another post with Three.js.
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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:
Visualizing a 200-node network w/ #Cypher + #D3js –cool method, but maybe a node quilt would work better? ow.ly/92wDE @maxdemarzi—
(@RiparianData) February 13, 2012
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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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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We’ve seen some pretty nice visualizations of nodes and their immediate neighbors, but we want to be able to visualize more. So we’re going to prepare a 200 node network, use Cypher to extract the data we want and visualize it with D3.js.
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