The other day, four Québécois gentlemen put out an arxiv preprint about one of my favorite topics*—algorithms for network epidemiology. In this blog post, I will try to lure you into: (1) Reading their manuscript. (2) Thinking about network-epi algorithm design. (3) Using my code for SIR (and theirs for SIS). *As a problem-solving exercise […]

This is a follow-up to my post a couple of days ago, about all the nitty-gritty when coding up a compartmental model for empirical temporal networks. I guess the question lingering is: How do you do it then? There is a very straightforward way, and an almost as simple, but much faster approach. If you […]

Here, I will discuss some technical issues of compartmental models in general, and the SIR model in particular, on temporal networks. These are things that feels a bit too off topic to even bother readers of papers, but everyone into network epidemiology need to consider them at some point. Maybe I should write a tutorial […]

If you’re a network scientist, or you’ve hung around with one, you’ve probably heard about ridiculograms. It’s a tongue-in-cheek derogative word for plots of huge networks intended to impress by their complexity. I have a vague memory of someone crediting Marc Vidal for coining it. In this blog post, we will explore if they really […]

In this blog post, I will explore why network science mostly is a science of large networks and argue that studying small networks can be just as rewarding and challenging as studying large ones. In other words, a manifesto for small-network science. (But a modest one, because it is not going to be any main […]

This is not a shot at making a side career as a motivational speaker. There are of course tons of more creative people out there, and even science about it! Still, I couldn’t resist writing down my thoughts since I slightly disagree with some of the gurus out there. 😛 1. Be bored   Good ideas […]

I really like computational social science and love to identify myself as a computational social scientist. Having an identity in that sense is perhaps not so important. Through my entire career, I’ve got accustomed to be a [one field] person doing [other field] things, and learned to like it. Still computational social science very much […]