172 citations · 516 across the 28 of their papers we have counts for
30 papers
Graph Signal Processing: History, Development, Impact, and Outlook
Geert Leus, Antonio G. Marques, José M. F. Moura +2
Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are ve…
GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
John Shi, Jose M. F. Moura
The paper presents the graph signal processing (GSP) companion model that naturally replicates the basic tenets of classical signal processing (DSP) for GSP. The companion model sh…
Learning Gradients of Convex Functions with Monotone Gradient Networks
Shreyas Chaudhari, Srinivasa Pranav, José M. F. Moura
While much effort has been devoted to deriving and analyzing effective convex formulations of signal processing problems, the gradients of convex functions also have critical appli…
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
Sérgio Machado, Anirudh Sridhar, Paulo Gil +3
We study the problem of graph structure identification, i.e., of recovering the graph of dependencies among time series. We model these time series data as components of the state…
The Companion Model -- a Canonical Model in Graph Signal Processing
John Shi, Jose M. F. Moura
This paper introduces a graph signal model defined by a graph and a shift, the graph and the $\t…
Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains
John Shi, Jose M. F. Moura
Vertex based and spectral based GSP sampling has been studied recently. The literature recognizes that methods in one domain do not have a counterpart in the other domain. This pap…