Good morning everyone,
This is just a gentle reminder about *today's seminar* on "Learning from signals on graphs with unobserved edges" by Michael Schaub (RWTH Aachen University). Abstract below. Please note that the seminar talk has been pushed back by one hour and *will take place at 18:00*.
The Zoom link for the talk is: https://us02web.zoom.us/j/87171939595
Hope to see you all there! Francesco and Nicola
---------------------------------------------- Speaker: Michael Schaub, RWTH Aachen University https://michaelschaub.github.io/
Title: Learning from signals on graphs with unobserved edges
Abstract: In many applications we are confronted with the following system identification scenario: we observe a dynamical process that describes the state of a system at particular times. Based on these observations we want to infer the (dynamical) interactions between the entities we observe. In the context of a distributed system, this typically corresponds to a "network identification" task: find the (weighted) edges of the graph of interconnections. However, often the number of samples we can obtain from such a process are far too few to identify the edges of the network exactly. Can we still reliably infer some aspects of the underlying system? Motivated by this question we consider the following identification problem: instead of trying to infer the exact network, we aim to recover a (low-dimensional) statistical model of the network based on the observed signals on the nodes. More concretely, here we focus on observations that consist of snapshots of a diffusive process that evolves over the unknown network. We model the (unobserved) network as generated from an independent draw from a latent stochastic blockmodel (SBM), and our goal is to infer both the partition of the nodes into blocks, as well as the parameters of this SBM. We present simple spectral algorithms that provably solve the partition and parameter inference problems with high-accuracy. We further discuss some possible variations and extensions of this problem setup.
This talk is part of NOMADS — Numerical ODEs, Matrix Analysis and Data Science — seminar at GSSI: https://num-gssi.github.io/seminar/
— Francesco Tudisco Assistant Professor School of Mathematics GSSI Gran Sasso Science Institute Web: https://ftudisco.gitlab.io