activity
20092025
most citedConnecting the Dots: Identifying Network Structure via Graph Signal Processing

385 citations · 1.8k across the 36 of their papers we have counts for

collaborators
Showing 2016Show all

6 papers · 1 filter

cs.IT2016

Rethinking Sketching as Sampling: A Graph Signal Processing Approach

Fernando Gama, Antonio G. Marques, Gonzalo Mateos +1

Sampling of signals belonging to a low-dimensional subspace has well-documented merits for dimensionality reduction, limited memory storage, and online processing of streaming netw…

math.OC2016

Two-Timescale Stochastic Dispatch of Smart Distribution Grids

Luis M. Lopez-Ramos, Vassilis Kekatos, Antonio G. Marques +1

Smart distribution grids should efficiently integrate stochastic renewable resources while effecting voltage regulation. The design of energy management schemes is challenging, one…

cs.SI2016

Network Topology Inference from Spectral Templates

Santiago Segarra, Antonio G. Marques, Gonzalo Mateos +1

We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between…

cs.IT2016★ 82 cited

Blind Identification of Graph Filters

Santiago Segarra, Gonzalo Mateos, Antonio G. Marques +1

Network processes are often represented as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of linear grap…

cs.SI2016

Network Topology Identification from Spectral Templates

Santiago Segarra, Antonio G. Marques, Gonzalo Mateos +1

Network topology inference is a cornerstone problem in statistical analyses of complex systems. In this context, the fresh look advocated here permeates benefits from convex optimi…

eess.SY2016★ 240 cited

Stationary Graph Processes and Spectral Estimation

Antonio G. Marques, Santiago Segarra, Geert Leus +1

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in m…