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 C5 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:

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


We propose a decentralised “local2global" approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or “patches") and training local representations for each patch independently. In a second step, we combine the local representations into a globally consistent representation by estimating the set of rigid motions that best align the local representations using information from the patch overlaps, via group synchronization.  A key distinguishing feature of local2global relative to existing work is that patches are trained independently without the need for the often costly parameter synchronisation during distributed training. This allows local2global to scale to large-scale industrial applications, where the input graph may not even fit into memory and may be stored in a distributed manner.

arXiv link:

2 November 2021
Franklin H. J. Kenter

Many real world graphs have edges correlated to the distance between them, but, in an inhomogeneous manner. While the Chung-Lu model and geometric random graph models both are elegant in their simplicity, they are insufficient to capture the complexity of these networks. For instance, the Chung-Lu model captures the inhomogeneity of the nodes but does not address the geometric nature of the nodes and simple geometric models treat names homogeneously.

In this talk, we develop a generalized geometric random graph model that preserves many graph-theoretic aspects of these models. Notably, each node is assigned a weight based on its desired expected degree; nodes are then adjacent based on a function of their weight and geometric distance. We will discuss the mathematical properties of this model. We also test the validity of this model on a graphical representation of the Drosophila Medulla connectome, a natural real-world inhomogeneous graph where spatial information is known.

This is joint work with Susama Agarwala, Johns Hopkins, Applied Physics Lab.

arXiv link:

9 November 2021

In the event of food-borne disease outbreaks, conventional epidemiological approaches to identify the causative food product are time-intensive and often inconclusive. Data-driven tools could help to reduce the number of products under suspicion by efficiently generating food-source hypotheses. We frame the problem of generating hypotheses about the food-source as one of identifying the source network from a set of food supply networks (e.g. vegetables, eggs) that most likely gave rise to the illness outbreak distribution over consumers at the terminal stage of the supply network. We introduce an information-theoretic measure that quantifies the degree to which an outbreak distribution can be explained by a supply network’s structure and allows comparison across networks. The method leverages a previously-developed food-borne contamination diffusion model and probability distribution for the source location in the supply chain, quantifying the amount of information in the probability distribution produced by a particular network-outbreak combination. We illustrate the method using supply network models from Germany and demonstrate its application potential for outbreak investigations through simulated outbreak scenarios and a retrospective analysis of a real-world outbreak.

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.


Simply send an empty email to