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 virtually on Zoom and 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.
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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.
In case you missed any of the talks, we will also make recordings of the talks available on our youtube channel.
Networks are an imperfect representation of a dataset, yet often there is little consideration for how these imperfections may affect network evolution and structure.
In this talk, I want to discuss a simple set of generative network models in which the mechanism of network growth is decomposed into two layers. The first layer represents the “observed” network, corresponding to our conventional understanding of a network. Here I want to consider the scenario in which the network you observe is not self-contained, but is driven by a second hidden network, comprised of the same nodes but different edge structure. I will show how a range of different network growth models can be constructed such that the observed and hidden networks can be causally decoupled, coupled only in one direction, or coupled in both directions.
One consequence of such models is the emergence of abrupt transitions in observed network topology – one example results in scale-free degree distributions which are robust up to an arbitrarily long threshold time, but which naturally break down as the network grows larger. I will argue that such examples illustrate why we should be wary of an overreliance on static networks (measured at only one point in time), and will discuss other possible implications for prediction on networks.
- Networks Seminar
Empirical networks often exhibit different meso-scale structures, such as community and core-periphery structure. Core-periphery typically consists of a well-connected core, and a periphery that is well-connected to the core but sparsely connected internally. Most core-periphery studies focus on undirected networks. In this talk we discuss a generalisation of core-periphery to directed networks which yields a family of core-periphery blockmodel formulations in which, contrary to many existing approaches, core and periphery sets are edge-direction dependent. Then we shall focus on a particular structure consisting of two core sets and two periphery sets, and we introduce two measures to assess the statistical significance and quality of this structure in empirical data, where one often has no ground truth. The idea will be illustrated on three empirical networks -- faculty hiring, a world trade data-set, and political blogs.
This is based on joint work with Andrew Elliott, Angus Chiu, Marya Bazzi and Mihai Cucuringu, available at https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2019.0783
- Networks Seminar
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