Speaker |
Anastasia Mantziou |
|---|---|
Affiliation |
Alan Turing Institute |
Date |
Friday, 13 October 2023 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
There is increasing appetite for analysing populations of network data due to the fast-growing body of applications demanding such methods. While methods exist to provide readily interpretable summaries of heterogeneous network populations, these are often descriptive or ad hoc, lacking any formal justification. In contrast, principled analysis methods often provide results difficult to relate back to the applied problem of interest. Motivated by two complementary applied examples, we develop a Bayesian framework to appropriately model complex heterogeneous network populations, whilst also allowing analysts to gain insights from the data, and make inferences most relevant to their needs. The first application involves a study in Computer Science measuring human movements across a University. The second analyses data from Neuroscience investigating relationships between different regions of the brain. While both applications entail analysis of a heterogeneous population of networks, network sizes vary considerably. We focus on the problem of clustering the elements of a network population, where each cluster is characterised by a network representative. We take advantage of the Bayesian machinery to simultaneously infer the cluster membership, the representatives, and the community structure of the representatives, thus allowing intuitive inferences to be made. The implementation of our method on the human movement study reveals interesting movement patterns of individuals in clusters, readily characterised by their network representative. For the brain networks application, our model reveals a cluster of individuals with different network properties of particular interest in Neuroscience. The performance of our method is additionally validated in extensive simulation studies. |
Biography |
Anastasia is a Postdoctoral Research Associate at The Alan Turing Institute supervised by Gesine Reinert and Mihai Cucuringu from the University of Oxford. Prior to that, she was a Research Assistant in statistical cyber-security at Imperial College London. She completed her PhD in Statistics at Lancaster University under the supervision of Dr Simon Lunagomez, Dr Robin Mitra and Professor Paul Fearnhead. Her research interests include network analysis, Bayesian methods and topic modelling. Her research has been applied to networks emerging from various scientific fields such as neuroscience, ecology and computer science (human tracking systems). Anastasia is currently working on network time series data with application on economics, under the economic networks and transaction data project in The Alan Turing Institute. |