Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Graph learning from data is a canonical problem that has received substantial attention in
the literature. Learning a structured graph is essential for interpretability and identification of …

An online algorithm for missing data imputation for networks with signals defined on the
edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we …

This paper addresses the problem of identifying the graph structure of a dynamical network
using measured input/output data. This problem is known as topology identification and has …

A critical challenge in graph signal processing is the sampling of bandlimited graph signals;
signals that are sparse in a well-defined graph Fourier domain. Current works focused on …

As irregularly structured data representations, graphs have received a large amount of
attention in recent years and have been widely applied to various real-world scenarios such …

Topology embodies the structure complexity of dynamical network coupled with interactive
individuals and plays a key role in its evolution dynamics. In many practical applications, the …

Performing signal processing over graphs requires knowledge of the underlying fixed
topology. However, graphs often grow in size with new nodes appearing over time, whose …

Graph-based learning and estimation are fundamental problems in various applications
involving power, social, and brain networks, to name a few. While learning pair-wise …

This paper considers a joint multi-graph inference and clustering problem for simultaneous
inference of node centrality and association of graph signals with their graphs. We study a …