24 citations · 24 across the 1 of their papers we have counts for
2 papers
cs.LG2024
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Jhony H. Giraldo, Aref Einizade, Andjela Todorovic +4
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-sca…
cs.LG2024★ 24 cited
Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction
Jhon A. Castro-Correa, Jhony H. Giraldo, Mohsen Badiey +1
Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from mi…