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