In one of my first network projects, as a student, I studied how networks break down when you remove edges in order of their betweenness. Simultaneously, Girvan and Newman used exactly the same approach to make the first modern community detection algorithm. This was before authors used to make their code publicly available, so when […]

We scientists love the word “hierarchy”. In every professor, it evokes a picture of us chalking up a pyramid on the black board and confidently explaining “at the top, we have the …” Hierarchies are systematic and meaningful orderings. They are the successful ends of research projects; bringers of peace to our curious minds. They […]

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 […]