activity
20162026
most citedGegenbauer Graph Neural Networks for Time-varying Signal Reconstruction

24 citations · 26 across the 21 of their papers we have counts for

collaborators

29 papers

cs.LG2026

Spatiotemporal Imputation with Graph-Informed Flow Matching

Zepeng Zhang, Aref Einizade, Jhony H. Giraldo +1

Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning appr…

cs.LG2026

Scaling Higher-Order Graph Learning with Maximal Clique Complexes

Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros +1

Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poo…

cs.LG2026

Generalization Bounds for Spectral GNNs via Fourier Domain Analysis

Vahan A. Martirosyan, Daniele Malitesta, Hugues Talbot +2

Spectral graph neural networks learn graph filters, but their behavior with increasing depth and polynomial order is not well understood. We analyze these models in the graph Fouri…

cs.CV20261 cited

WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift

Julian D. Santamaria, Claudia Isaza, Jhony H. Giraldo

Wildlife monitoring is crucial for studying biodiversity loss and climate change. Camera trap images provide a non-intrusive method for analyzing animal populations and identifying…

cs.LG2025

Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention

Lucas Li, Jean-Baptiste Puel, Florence Carton +2

Perovskite solar cells are promising candidates for next-generation photovoltaics. However, their performance as multi-scale devices is determined by complex interactions between t…

cs.LG2025

Second-Order Tensorial Partial Differential Equations on Graphs

Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Processing data on multiple interacting graphs is crucial for many applications, but existing approaches rely mostly on discrete filtering or first-order continuous models, dampeni…