16 citations · 39 across the 21 of their papers we have counts for
5 papers · 1 filter
Structure-Guided Input Graph for GNNs facing Heterophily
Victor M. Tenorio, Madeline Navarro, Samuel Rey +2
Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets,…
Online Network Inference from Graph-Stationary Signals with Hidden Nodes
Andrei Buciulea, Madeline Navarro, Samuel Rey +2
Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneo…
Redesigning graph filter-based GNNs to relax the homophily assumption
Samuel Rey, Madeline Navarro, Victor M. Tenorio +2
Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is rep…
Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due t…
Mitigating Subpopulation Bias for Fair Network Topology Inference
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subp…