Efficient Graph Signal Recovery over Big Networks

Gabor Hannak, Peter Berger, Gerald Matz, Alex Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


We consider the problem of recovering a smooth graph signal from noisy samples taken at a small number of graph nodes. The recovery problem is formulated as a convex optimization problem which minimizes the total variation (accounting for the smoothness of the graph signal) while controlling the empirical error. We solve this total variation minimization problem efficiently by applying a recent algorithm proposed by Nesterov for non-smooth optimization problems. Furthermore , we develop a distributed implementation of our algorithm and verify the performance of our scheme on a large-scale real-world dataset.
Original languageEnglish
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Number of pages5
ISBN (Electronic)9781538639542
Publication statusPublished - 1 Mar 2017
MoE publication typeA4 Article in a conference publication
EventAsilomar Conference on Signals, Systems & Computers - Pasific Grove, United States
Duration: 6 Nov 20169 Nov 2016
Conference number: 50

Publication series

NameConference Record of the Asilomar Conference on Signals Systems and Computers
ISSN (Print)1058-6393


ConferenceAsilomar Conference on Signals, Systems & Computers
Abbreviated titleASILOMAR
Country/TerritoryUnited States
CityPasific Grove
Internet address


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