I recently decided I wanted try presenting my work at an academic conference, so I had to write and submit an abstract for my proposed presentation. The problem, of course, was that I had no idea how to write a scientific abstract, which was a bit of a problem. Most of the advice I received and could find online was too vague for my taste, so I decided to take matters into my own hands and deconstruct some abstracts to see how those authors did it. And I’m really glad I did, because it was very enlightening.

You know the drill. In the last episode I did of this series, I said the next one would be coming shortly, huh?

Yeah, right. I lied. These come out when they come out.

Here’s another linkdump—a list of links to stuff I thought was interesting but likely won’t get to properly reviewing any time soon.¹

I reserve the right to more fully review any of these article at a later date, of course. (Though at this point, it’s extremely unlikely to ever happen.)

Here is a long list of sources I consulted at various points while doing research for this project. As it might have become clear through reading the previous installments of this series, this was more a talk about the densest subgraph problem than it was about theory being useful in computer science. There are a few reasons for that, which I might explain in more detail at some point in the future. This means that I presented a lot of information that I borrowed from other people! Here’s an annotated bibliography of sorts, in case you were interested in going deeper, for whatever reason.

In Part 3, we discussed the densest subgraph problem (DSP) and some algorithms for solving it. In this section, we’ll be looking at how we can generalize this problem and those algorithms to solve some other problems. This is the part of the talk where I will introduce some fancy new math I had to learn in order to understand how one might generalize iterative peeling to solve adjacent problems.