no code implementations • 22 Mar 2024 • Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
We consider fair network topology inference from nodal observations.
no code implementations • 16 Dec 2023 • Andrei Buciulea, Elvin Isufi, Geert Leus, Antonio G. Marques
Graphs are widely used to represent complex information and signal domains with irregular support.
no code implementations • 30 Jun 2023 • Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations.
no code implementations • 13 Mar 2023 • Andrei Buciulea, Antonio G. Marques
Graphs have become pervasive tools to represent information and datasets with irregular support.
1 code implementation • 4 Dec 2022 • Samuel Rey, Madeline Navarro, Andrei Buciulea, Santiago Segarra, Antonio G. Marques
Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables.
1 code implementation • 5 Oct 2021 • Samuel Rey, Andrei Buciulea, Madeline Navarro, Santiago Segarra, Antonio G. Marques
Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference.