2 papers
cs.LG2026
Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
Antonis Vasileiou, Juan Cervino, Pascal Frossard +7
Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine lea…
cs.LG2025
Graph Semi-Supervised Learning for Point Classification on Data Manifolds
Caio F. Deberaldini Netto, Zhiyang Wang, Luana Ruiz
We propose a graph semi-supervised learning framework for classification tasks on data manifolds. Motivated by the manifold hypothesis, we model data as points sampled from a low-d…