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.
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.