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. 

The Networks seminars usually take place on Tuesdays at 12:00-13:00 in C1 in the Maths Institute.
A full schedule of the upcoming talks can be found below.

To sign up to our mailing list simply send an empty email to the following address:

If you would like to give a presentation at our seminar, please do not hesitate to contact the organisers Karel Devriendt or Rodrigo Leal Cervantes. The presentation can be either about your own work or on some (recent) interesting article on networks or on complex systems in general.

We also have a webpage on the CABDyN website, but sadly this is no longer maintained.

Upcoming Seminars


It is known that many real-world networks exhibit geometric properties.  Brain networks, social networks, and wireless communication networks are a few examples.  Storage and transmission of the information contained in the topologies and structures of these networks are important tasks, which, given their scale, is often nontrivial.  Although some (but not much) work has been done to characterize and develop compression limits and algorithms for nonspatial graphs, little is known for the spatial case.  In this talk, we will discuss an information theoretic formalism for studying compression limits for a fairly broad class of random geometric graphs.  We will then discuss entropy bounds for these graphs and, time permitting, local (pairwise) connection rules that yield maximum entropy properties in the induced graph distribution.

25 February 2020
Marya Bazzi

Multilayer networks are a way to represent dependent connectivity patterns — e.g., time-dependence, multiple types of interactions, or both — that arise in many applications and which are difficult to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as communities, to discover features that lie between the microscale and the macroscale. We introduce a framework for the construction of generative models for mesoscale structure in multilayer networks.  We model dependency at the level of partitions rather than with respect to edges, and treat the process of generating a multilayer partition separately from the process of generating edges for a given multilayer partition. Our framework can admit many features of empirical multilayer networks and explicitly incorporates a user-specified interlayer dependency structure. We discuss the parameters and some properties of our framework, and illustrate an example of its use with benchmark models for multilayer community-detection tools. 


You can also find a list of all talks (with abstracts) prior to 2018 here.


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