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 C4 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:
oxford_networks_seminar-subscribe@maillist.ox.ac.uk

If you would like to give a presentation at our seminar, please do not hesitate to contact the organiser Florian Klimm. 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We also have a webpage on the CABDyN website, but sadly this is no longer maintained.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

17 September 2019
12:00
Florian Klimm
Abstract

In this seminar, I first discuss a paper by Aslak et al. on the detection of intermittent communities with the Infomap algorithm. Second, I present own work on the detection of intermittent communities with modularity-maximisation methods. 

Many real-world networks represent dynamic systems with interactions that change over time, often in uncoordinated ways and at irregular intervals. For example, university students connect in intermittent groups that repeatedly form and dissolve based on multiple factors, including their lectures, interests, and friends. Such dynamic systems can be represented as multilayer networks where each layer represents a snapshot of the temporal network. In this representation, it is crucial that the links between layers accurately capture real dependencies between those layers. Often, however, these dependencies are unknown. Therefore, current methods connect layers based on simplistic assumptions that do not capture node-level layer dependencies. For example, connecting every node to itself in other layers with the same weight can wipe out dependencies between intermittent groups, making it difficult or even impossible to identify them. In this paper, we present a principled approach to estimating node-level layer dependencies based on the network structure within each layer. We implement our node-level coupling method in the community detection framework Infomap and demonstrate its performance compared to current methods on synthetic and real temporal networks. We show that our approach more effectively constrains information inside multilayer communities so that Infomap can better recover planted groups in multilayer benchmark networks that represent multiple modes with different groups and better identify intermittent communities in real temporal contact networks. These results suggest that node-level layer coupling can improve the modeling of information spreading in temporal networks and better capture intermittent community structure.

Aslak, Ulf, Martin Rosvall, and Sune Lehmann. "Constrained information flows in temporal networks reveal intermittent communities." Physical Review E 97.6 (2018): 062312.

 

8 October 2019
12:00
Marya Bazzi
Abstract

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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