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:
@email

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, 04 Nov 2025
14:00
C4

Exploring partition diversity in complex networks

Lena Mangold
(IT:U Interdisciplinary Transformation University Austria)
Abstract
Partition diversity refers to the concept that for some networks there may be multiple, similarly plausible ways to group the nodes, rather than one single best partition. In this talk, I will present two projects that address this idea from different but complementary angles. The first introduces the benchmark stochastic cross-block model (SCBM), a generative model designed to create synthetic networks with two distinct 'ground-truth' partitions. This allows us to study the extent to which existing methods for partition detection are able to reveal the coexistence of multiple underlying structures. The second project builds on this benchmark and paves the way for a Bayesian inference framework to directly detect coexisting partitions in empirical networks. By formulating this model as a microcanonical variant of the SCBM, we can evaluate how well it fits a given network compared to existing models. We find that our method more reliably detects partition diversity in synthetic networks with planted coexisting partitions, compared to methods designed to detect a single optimal partition. Together, the two projects contribute to a broader understanding of partition diversity by offering tools to explore the ambiguity of network structure.
Tue, 11 Nov 2025
14:00
C4

Towards Precision in the Diagnostic Profiling of Patients: Leveraging Symptom Dynamics in the Assessment and Treatment of Mental Disorders

Omid Ebrahimi
(Department of Experimental Psychology, University of Oxford)
Abstract

Major depressive disorder (MDD) is a heterogeneous mental disorder. International guidelines present overall symptom severity as the key dimension for clinical characterisation. However, additional layers of heterogeneity may reside within severity levels related to how symptoms interact with one-another in a patient, called symptom dynamics. We investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same diagnosis and overall symptom severity. We show that examining symptom dynamics provides information about the person-specific psychopathological expression of patients beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may serve as a promising new dimension for clinical characterisation. Areas of opportunity are outlined for the field of precision psychiatry in uncovering disorder evolution patterns (e.g., spontaneous recovery; critical worsening) and the identification of granular treatment effects by moving toward investigations that leverage symptom dynamics as their foundation. Future work aimed at investigating the cascading dynamics underlying depression onset and maintenance using the large-scale (N > 5.5 million) CIPA Study are outlined. 

Tue, 02 Dec 2025
14:00
C4

TBA

Fabio Caccioli
(University College London)
Abstract

TBA

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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Last updated on 29 Nov 2024, 12:47pm. Please contact us with feedback and comments about this page.