Abstract
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 language | English |
|---|---|
| Title of host publication | 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| Pages | 200-204 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-153864410-2 |
| ISBN (Print) | 978-1-5386-4411-9 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | DSW 2018: IEEE Data Science Workshop - Lausanne, Switzerland Duration: 4 Jun 2018 → 6 Jun 2018 |
Conference
| Conference | DSW 2018 |
|---|---|
| Country/Territory | Switzerland |
| City | Lausanne |
| Period | 4/06/18 → 6/06/18 |
Bibliographical note
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-careOtherwise 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.
Keywords
- graph learning
- Graph signal processing
- network deconvolution
- network topology inference
- sparse graphs