I’ve had a secret project running in the background
this week two weeks ago (how time flies!), a continuation of my work on bias reduction for traceroute sampling. It would be nice if this had applications to global health, but unfortunately (and uncharacteristically) I can’t think of any. It is a great opportunity for visualizing networks, though, a topic worthy of a quick post.
The bowl-of-spaghetti network visualization has been a staple of complex networks research for the last decade. I’m not convinced that there is anything interesting to learn from real world networks by drawing them in 2 or 3 dimensions, but the graphics a seriously eye catching. And I’m not convinced that there isn’t anything to learn from them, either. I invite you to convince me in the comments.
What my side project has reminded me of, however, is the value of drawing networks in 2 dimensions for illustrating the principles of network algorithms and network statistics. And if the topic of study is complex or real-world or random networks, than a little bit of spaghetti in the graphic seems appropriate.
As for what to graph, here are my thoughts. The Erdos-Renyi graph doesn’t look good, and the Preferential Attachment graph doesn’t look good. Use them for your theorems and for your simulations, but when it comes time to draw something, consider a random geometric graph. And since these can be a little dense, you might want an “edge-percolated random geometric graph”.
I did have a little trouble with this approach, too, when I was drawing minimum spanning trees, because the random geometric points end up being placed really close together occasionally. So maybe the absolutely best random graph for illustrations would be a geometric graph with vertices from a “hard core” model, which is to say random conditioned on being a minimum distance apart. Unfortunately, it is an open question how to efficiently generate hard-core points. But it’s not hard to fake:
Want some of your own? Here’s the code.