This is a comment on the recent arxiv by Voitalov, van der Hoorn, van der Hofstad, and Krioukov titled Scale-free networks well done, and the ongoing debate of scale-free networks. As usual, I take a laid-back spectator position—no papers, no research of my own, just another blog post of my personal reading of this contribution […]

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 precisely 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 blackboard 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 connect […]

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, […]

Now I turned this blog post into an arXiv preprint: https://arxiv.org/abs/2007.14386 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 are a very straightforward way […]

Now I turned this blog post into an arXiv preprint: https://arxiv.org/abs/2007.14386 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 feel a bit too off-topic to even bother readers of papers, but everyone into network epidemiology needs to consider […]

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 […]

igatnvestIn this blog post, I will explore why network science is mostly 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. Throughout my entire career, I’ve got accustomed to being a [one field] person doing [another field] things, and learned to like it. Still computational social science very much […]