Networks seminar

Welcome to the homepage of the Networks seminars, a weekly seminar series on networks, complex systems, and related topics held in the Mathematical Institute. In this year's series, we will alternate between regular talks and "fresh from the arXiv" talks (FFTA) in which we invite the author of a recently published (pre)print to discuss their work. Suggestions are always welcome!

The Networks seminar usually takes place on Tuesdays at 14:00-15:00. In line with current regulation, we are excited to announce that the seminars will now run with a new hybrid format that will allow attendees to choose whether to join our group in person in room C1 at the Mathematical Institute, or to attend remotely on Zoom. A link to the event will be made available in the schedule of upcoming talks below (for logged-in users) and via the mailing list.

To sign up to our mailing list simply send an empty email to the following address:
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If you would like to give a presentation at our seminar, please do not hesitate to contact the organisers Erik Hörmann and Yu Tian. The presentation can be either about your own work or on some (recent) interesting article on networks or on complex systems in general.

In case you missed any of the talks, we will also make recordings of the talks available on our youtube channel.

Upcoming Seminars

Tue, 03 Mar 2026

14:00 - 15:00

Explaining order in non-equilibrium steady states

Dr. Jacob Calvert

(Sante Fe Institute)

Abstract

Statistical mechanics explains that systems in thermal equilibrium spend a greater fraction of their time in states with apparent order because these states have lower energy. This explanation is remarkable, and powerful, because energy is a "local" property of states. While non-equilibrium steady states can similarly exhibit order, there can be no local property analogous to energy that explains why, as Landauer argued 50 years ago. However, recent experiments suggest that a broad class of non-equilibrium steady states satisfy an approximate analogue of the Boltzmann distribution, with tantalizing possibilities for basic and applied science.

I will explain how this analogue can be viewed as one of several approximations of Markov chain stationary distributions that arise throughout network science, random matrix theory, and physics. In brief, this approximation "works" when the correlation between a Markov chain's effective potential and the logarithm of its exit rates is high. It is therefore important to estimate this correlation for different classes of Markov chains. I will discuss recent results on the correlation exhibited by reaction kinetics on networks and dynamics of the Sherrington–Kirkpatrick spin glass, as well as highly non-reversible Markov chains with i.i.d. random transition rates. (Featuring joint work with Dana Randall and Frank den Hollander.)

Tue, 10 Mar 2026
14:00

TBA

Márton Pósfai

(Central European University)

You can also find a list of all talks (with abstracts) prior to 2018 here, and the former website
of the Networks journal club at the Oxford complexity center (CABDyN) here.

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