Lecture
LCN2 Seminar
- Date
- Friday 20 May 2022
- Time
- Location
- Snellius
- Room
- Room 407-409
54th LCN2 seminar
Speaker: Leto Peel (Maastricht University)
Title: Hierarchical community structure in networks
Abstract: Modular and hierarchical structures are pervasive in real-world complex systems. A great deal of effort has gone into trying to detect and study these structures. Important theoretical advances in the detection of modular, or "community", structures have included identifying fundamental limits of detectability by formally defining community structure using probabilistic generative models. Detecting hierarchical community structure introduces additional challenges alongside those inherited from community detection. Here we present a theoretical study on hierarchical community structure in networks, which has thus far not received the same rigorous attention. We address the following questions: 1) How should we define a valid hierarchy of communities? 2) How should we determine if a hierarchical structure exists in a network? and 3) how can we detect hierarchical structure efficiently? We approach these questions by introducing a definition of hierarchy based on the concept of stochastic externally equitable partitions and their relation to probabilistic models, such as the popular stochastic block model. We enumerate the challenges involved in detecting hierarchies and, by studying the spectral properties of hierarchical structure, present an efficient and principled method for detecting them.
About Leto Peel
Dr. Leto Peel is an Assistant Professor at the Department of Data Analytics and Digitalisation at the School of Business and Economics at Maastricht University in The Netherlands. Previously he was an FNRS chargé de recherche at the Université catholique de Louvain in Belgium and a postdoc at the University of Colorado at Boulder. He did his PhD at University College London while working as a senior research scientist at BAE Systems. His research interests are in complex networks, machine learning and statistical inference. He has 15 years of experience in theoretical and applied machine learning research in academia and industry. He has worked on many interdisciplinary research projects spanning a diverse variety of domains including biology, computer vision, crime science, economics, engineering, game theory, geography, geomatics, physics and security. His industry collaborations have included Airbus, BAE Systems and London MET Police and has collaborated with a variety of academic institutions (including Imperial, MIT, Oxford and the Santa Fe Institute). He has published in a number of top-tier computer science venues for machine learning and data mining (e.g. AAAI, ICDM, CVPR, SDM) as well as top interdisciplinary scientific journals (e.g. Science Advances, PNAS, Physical Review X).