Applied Seminar - "Exploiting Symmetries in Network Analysis", Dr Ruben Sanchez Garcia (University of Southampton) Seminar
- Time:
- 12:00 - 14:00
- Date:
- 13 November 2018
- Venue:
- Ketley Room, 4001, Building 54, School of Mathematical Sciences, University of Southampton, Highfield Campus, SO17 1BJ
For more information regarding this seminar, please email Dr Philip Greulich at P.S.Greulich@southampton.ac.uk .
Event details
Network models of real-world complex systems have been extremely successful at revealing structural and dynamical properties of these systems. One property of interest is the presence of structural redundancies, which manifest themselves as symmetries in a network model. It has been shown that real-world networks possess a large number of symmetries, and that this has important consequences for network structural, spectral and dynamical properties. Crucially, network symmetries are inherited by any measure or metric on the network, and any derived matrix such as the graph Laplacian.
In this talk, Ruben will present a complete theory for the study of symmetry in empirical networks and their effects on arbitrary network measures, and show how this can be exploited in practice in a number of ways, from redundancy compression, to computational reduction. We also uncover the spectral signatures of symmetry for an arbitrary network measure, predicting and explaining most of the discrete part of the spectrum of a network measure such as the graph Laplacian. We show that computing network symmetries and motifs is very efficient in practice, testing real-world examples up to several million nodes. Our theoretical framework generalise, and helps understand, other network symmetry results in the literature.
Speaker information
Dr Ruben Sanchez Garcia,Ruben is a Lecturer in Pure and Applied Mathematics since 2012. He has a pure mathematics background in Algebraic Topology, although he is currently working in the interface between pure and applied mathematics within complexity theory, particularly mathematical aspects of complex networks, and topological data analysis.