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 Sep 2023 • Victor M. Tenorio, Madeline Navarro, Santiago Segarra, Antonio G. Marques
We present a framework to recover completely missing node features for a set of graphs, where we only know the signals of a subset of graphs.
no code implementations • 14 Sep 2023 • Madeline Navarro, Santiago Segarra
The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations.
no code implementations • 13 Sep 2023 • Madeline Navarro, Camille Little, Genevera I. Allen, Santiago Segarra
Furthermore, our method allows us to use the generalization ability of mixup to improve both fairness and accuracy.
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.
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 • 27 Oct 2022 • Madeline Navarro, Santiago Segarra
Mixup is a data augmentation method to create new training data by linearly interpolating between pairs of data samples and their labels.
1 code implementation • 17 Sep 2022 • Madeline Navarro, Santiago Segarra
The proposed joint network and graphon estimation is further enhanced with the introduction of a robust method for noisy graph sampling information.
1 code implementation • 11 Feb 2022 • Madeline Navarro, Santiago Segarra
We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model.
no code implementations • 1 Nov 2021 • Madeline Navarro, Genevera I. Allen, Michael Weylandt
In this paper, we propose a convex approach for the task of network clustering.
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.
no code implementations • 16 Oct 2020 • Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra
Inferring graph structure from observations on the nodes is an important and popular network science task.
no code implementations • 15 Oct 2020 • T. Mitchell Roddenberry, Madeline Navarro, Santiago Segarra
In particular, we consider the case where the graph was drawn from a graphon model, and we supplement our convex optimization problem with a provably-valid regularizer on the spectrum of the graph to be recovered.