Sparsest Network Support Estimation: A Submodular Approach

Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus

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

5 Citations (Scopus)
5 Downloads (Pure)


In this work, we address the problem of identifying the underlying network structure of data. Different from other approaches, which are mainly based on convex relaxations of an integer problem, here we take a distinct route relying on algebraic properties of a matrix representation of the network. By describing what we call possible ambiguities on the network topology, we proceed to employ sub-modular analysis techniques for retrieving the network support, i.e., network edges. To achieve this we only make use of the network modes derived from the data. Numerical examples showcase the effectiveness of the proposed algorithm in recovering the support of sparse networks.

Original languageEnglish
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-153864410-2
ISBN (Print)978-1-5386-4411-9
Publication statusPublished - 2018
EventDSW 2018: IEEE Data Science Workshop - Lausanne, Switzerland
Duration: 4 Jun 20186 Jun 2018


ConferenceDSW 2018

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • graph learning
  • Graph signal processing
  • network deconvolution
  • network topology inference
  • sparse graphs


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