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 …

We address the problem of identifying the structure of an undirected graph from the
observation of signals defined on its nodes. Fundamentally, the unknown graph encodes …

Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …

Brain region-of-interest (ROI) segmentation based on structural magnetic resonance
imaging (MRI) scans is an essential step for many computer-aid medical image analysis …

Inferring graph structure from observations on the nodes is an important and popular
network science task. Departing from the more common inference of a single graph, we …

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

We address the problem of inferring a graph from nodal observations, which are modeled as
non-stationary graph signals generated by local diffusion dynamics that depend on the …

Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and
reveal insights into interactions amongst different brain regions. However, most connectivity …

We address the problem of inferring an undirected graph from nodal observations, which are
modeled as non-stationary graph signals generated by local diffusion dynamics that depend …

In this article, we explore the state-space formulation of a network process to recover from
partial observations the network topology that drives its dynamics. To do so, we employ …