3 papers
eess.SP2026
Sampling Transferable Graph Neural Networks with Limited Graph Information
Haoyu Wang, Renyuan Ma, Gonzalo Mateos +1
Graph neural networks (GNNs) achieve strong performance on graph learning tasks, but training on large-scale networks remains computationally challenging. Transferability results s…
eess.SP2025
Dirichlet Meets Horvitz and Thompson: Estimating Homophily in Large Networks via Sampling
Hamed Ajorlou, Gonzalo Mateos, Luana Ruiz
Assessing homophily in large-scale networks is central to understanding structural regularities in graphs, and thus inform the choice of models (such as graph neural networks) adop…
stat.ML2025
A Generative Model for Controllable Feature Heterophily in Graphs
Haoyu Wang, Renyuan Ma, Gonzalo Mateos +1
We introduce a principled generative framework for graph signals that enables explicit control of feature heterophily, a key property underlying the effectiveness of graph learning…