Network topology inference is a significant problem in network science. Most graph signal
processing (GSP) efforts to date assume that the underlying network is known and then …

Network-topology inference from (vertex) signal observations is a prominent problem across
data-science and engineering disciplines. Most existing schemes assume that observations …

This paper looks at the task of network topology inference, where the goal is to learn an
unknown graph from nodal observations. One of the novelties of the approach put forth is the …

We propose a sampling algorithm to perform system identification from a set of input-output
graph signal pairs. The dynamics of the systems we study are given by a partially known …

Learning graphs from sets of nodal observations represents a prominent problem formally
known as graph topology inference. However, current approaches are limited by typically …

Learning graph structure from observed signals over graph is a crucial task in many graph
signal processing (GSP) applications. Existing approaches focus on inferring static graph …

Topology inference is a crucial problem for cooperative control in multi-agent systems.
Different from most prior works, this paper is dedicated to inferring the directed network …

This dissertation is divided in two parts. In Part I, we present a method to characterize
functional connectivity between sites in the cerebral cortex of primates using a novel …