This post is revised after comments from Urska Demsar, Des Higham, Mason Porter, Jan Peter Schäfermeyer, Johan Ugander, and Jean-Gabriel Young. Thanks! Our field is interdisciplinary and many smart people have been thinking about similar things. No wonder things get reinvented and rediscovered many times. I don’t think science is a competition to get good […]
The importance of being earnest about the importance of nodes
One of the problems network science sets out to solve is to find important nodes. Of course, what is important depends on the context, but an applied scientist coming to network science for an answer probably has a clear idea what it means in her study system. There is no shortage of methods in the […]
Steak-pun networks: The state of affairs
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 own reading of this contribution […]
Community detection: A consumer’s voice
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 […]
Hierarchies and networks
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 art, sport and science of network-epidemic algorithms
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 […]
Crazy fast code for SIR on temporal networks
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 […]
