Graphs are a fundamental computer science datatype, and graphs show up in all sorts of models in all sorts of places. So when you have a graph, what can you do with it? Particularly if it is really big?

Thirty minutes isn't a lot of time to discuss graph processing as a topic, so there won't be a lot of discussion relative to graph theory generally or the terminology of graphs. Instead, this is inspired by Raymond Hettinger's "mastering team play" - a series of exercises showing the lowering of a problem into a graph representation, followed by a demonstration of how the problem can be solved through graph processing. There will also be a little bit of compare-and-contrast between the available graph libraries to show differences. Each problem will be given 8-10 minutes.

Problem 1: Python's (legal) history

Python has developed over time under a number of organizations - each with their own license. What portions of Python's codebase are under each license?

• The CVS/SVN/HG trees as graphs modeling change in time
• Identifying and labeling node types
• Graphing and reporting on results

Problem 2: Development Cliques

Linux is famously developed with "lieutenants" in charge of different subsystems of the kernel. Python doesn't have lieutenants... or does it? Put another way, if you have a patch, who should you submit it to?

• Mailing list connections as a graph
• Analysis of connections, cliques, and centrality
• Graphing and reporting on results

Problem 3: Let's get social

Your employer has decided that its website should be turned into a social network - you know, because there aren't enough of those.

• Bootstrapping a graph by looking at pairwise analysis of products
• How to suggest who people "might know"?