3 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…
cs.LG2024
Improved Image Classification with Manifold Neural Networks
Caio F. Deberaldini Netto, Zhiyang Wang, Luana Ruiz
Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical…