Random Networks Workshop
21st May, 2025
The University of Sheffield Probability Group invites you to attend a one-day workshop on the broad topic of Random Networks.
This workshop is the third in a series which aims to bring together academics interested in random networks regularly to hear about exciting advancements in the field. This workshop previously ran in 2022 and 2024. Plans are in place to run this workshop again in 2026.
Please see below for details of this workshop as well as information about how to register.
Programme
09:00 - 09:30: Registration (tea/coffee + Pastries, catered by PJ Taste.)
Title: to come
Abstract: to come
10:20 - 10:50: Refreshments (tea/coffee + biscuits, catered by PJ Taste.)
Title: to come
Abstract: to come
11:40 - 11:50: Break
11:50 - 12:40: Open problem session (call for EOIs on registration form)
Problem 1: tbc
Problem 2: tbc
Problem 3: tbc
Problem 4: tbc
Problem 5: tbc
12:40 - 13:40: Lunch (Sandwiches, catered by PJ Taste.)
13:40 - 14:40: Contributed talks by PhD students (call for EOIs on registration form)
Title: Entropy of High Dimensional Random Geometric Graphs
Abstract: In the modern age, high dimensional data is becoming more and more common. In order to model such data, it is important to understand the geometry of and the distance between data points in high dimensional space. We extend a multivariate central limit theorem for distance distributions in high dimensional cubes to high dimensional tori, and show that translation invariance means that the distribution of random geometric graphs (RGGs) on the torus limits to Erdős–Rényi ensemble, whereas the distribution of RGGs on a cube tends to a fundamentally different limit, with lower entropy. We then attempt to introduce higher-order corrections to the CLT to approximate the entropy of RGGs in lower dimensional space.
Title: Dissimilar Batch Decompositions of Random Datasets
Abstract: For better learning, large datasets are often split into small batches and fed sequentially to the predictive model. In this talk, we study such batch decompositions from a probabilistic perspective. We assume that data points are drawn independently from a given space and define a concept of similarity between two data points. We then consider decompositions that restrict the amount of similarity within each batch and obtain high probability bounds for the minimum size. We demonstrate an inherent tradeoff between relaxing the similarity constraint and the overall size and also use martingale methods to obtain bounds for the maximum size of data subsets with a given similarity.
14:40 - 15:10: Refreshments (tea/coffee + biscuits, catered by PJ Taste.)
Title: to come
Abstract: to come
16:00 - 16:10: Break
Title: to come
Abstract: to come
Registration Link
https://forms.gle/qSqvP51SvhPFQ1SP8
Registration is open and will close in early May.
Location
All talks will take place in Lecture Theatre A of the School of Mathematics and Statistics located in Hicks Building. This room is located at the south entrance to the Hicks Building by the Harley Pub (down the hill from the main entrance.) Helpful images are below.
Address: Hicks Building, Hounsfield Road, Sheffield, S3 7RH
The red box on the right hand side is Sheffield's train station. The red circle on the left is the School of Mathematics and Statistics. Distance between the two is a 15-20 minute walk.
Here you can see the School of Mathematics and Statistics (Hicks Building) and a red arrow indicating the entrance where you will find Lecture Theatre A. The Black pentagon is where lunch will be served.
Hotels in the area
Below are a few hotels in the area for those of you who intend to stay overnight in Sheffield.
Funding bodies
This meeting is generously supported by a grant from the Heilbronn Institute for Mathematical Research and the Engineering and Physical Sciences Research Council. The University of Sheffield has supplied physical and online facilities for this workshop. The Applied Probability Trust is providing administrative and organisational support for this meeting.
