What should a graph look like and how can I tell two graphs apart?
On Twitter Claire Willett from Riparian Data asked:
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.
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.
We are going to take one of their example visualizations and visualize a follows graph.
To create our graph, we will take the names of 20 people: create nodes for them, add them to an index, and randomly link them together.
If you’re not so lucky and are working on pet projects by yourself then you’ll have to do it all. So I wanted to give you a little nudge into learning a visualization framework. Since my most popular blog post so far has been Graph Visualization and Neo4j and we’ve already seen one example that you’ll probably want to customize in your projects, we’ll stick with processing.js, and in the future I can do a little intro on D3.js, Unveil.js and maybe something a little crazier like VVVV.js.
<!DOCTYPE html> <html> <head> <title>Hello World - Processing.js</title> <script src="processing-1.3.6.min.js"></script> </head> <body> <canvas data-src="helloworld.pjs"></canvas> </body> </html>
All right, let’s create the helloworld.pjs we reference as our canvas data source.
So far we’ve learned how to get Neo4j up and running with Neography, how to find friends of friends and degrees of separation with the Neo4j REST API and a little bit of the Gremlin and Cypher languages. However, all we’ve seen is text output. We haven’t really “seen” a graph yet, but that’s about to change.
Vouched holds a graph of skill specific recommendations people have made to each other and visualizes it. I extracted the visualization, and hosted it on github as neovigator. You can get your very own visualization up and running or take a look at this instance running on Heroku.