Distributed sparsified graph filters for denoising and diffusion tasks

Elvin Isufi*, Geert Leus

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

8 Citations (Scopus)


Generally in distributed signal processing, and specifically in distributed graph filters, reducing the communication and computational complexity plays a key role in the network lifetime. In this work we present a novel algorithm to sparsify the graph filtering operation in a random way, where each node decides locally with a certain probability with which of its neighbors to communicate. We show that, if the filter coefficients are changed accordingly, the first and second order moment of the stochastic output are identical to the deterministic filter output and bounded, respectively. We apply our idea on the tasks of signal denoising and diffusion. Numerical results show that the distributed implementation costs of the filter can be reduced up to 95% with a variance of 10-3 from the deterministic output.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-1-5090-4117-6
Publication statusPublished - 2017
EventICASSP 2017: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals - Hilton New Orleans Riverside, New Orleans, LA, United States
Duration: 5 Mar 20179 Mar 2017
Conference number: 42


ConferenceICASSP 2017
Abbreviated titleICASSP
Country/TerritoryUnited States
CityNew Orleans, LA
Internet address


  • diffusion graph signals
  • graph filters
  • graph signal denoising
  • graph signal processing
  • graph sparsification


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