Fundamental limits of learning in community structure

Area of research: Random graphs, network theory, probability

Department: Department of Mathematics

Project title: Fundamental limits of learning in community structure

Project summary

A key challenge in AI and machine learning is to find limits to what one can 'learn' about a data set: what can be realistically inferred about noisy data. Here we represent the data as a network and ask whether we can detect the difference between two networks, one of which contains communities which represent structure in the data. This project is to investigate in what circumstances it is possible for any learning model to detect the communities in non-perfect data.

What do you look forward to the most during your sabbatical?

The opportunity to have dedicated time to think on this project and to interact with my visitors and other sabbatical holders. I think it's important to be always talking to researchers using network data to inform their research to ensure we study the network properties of interest and that we consider useful models of the errors in network data.