Sparsest Network Support Estimation

Sparsest Network Support Estimation: A Submodular Approach

Coutino, Mario (TU Delft Signal Processing Systems)
Chepuri, S.P. (TU Delft Signal Processing Systems)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2018

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.

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

http://resolver.tudelft.nl/uuid:8f58e13e-f215-40cf-9709-3bd10270d844

https://doi.org/10.1109/DSW.2018.8439890

IEEE, Piscataway, NJ

2019-02-20

978-1-5386-4411-9

2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

DSW 2018, 2018-06-04 → 2018-06-06, Lausanne, Switzerland

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care 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.

Institutional Repository

conference paper

© 2018 Mario Coutino, S.P. Chepuri, G.J.T. Leus