14 papers
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…
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…
Feature-Aware (Hyper)graph Generation via Next-Scale Prediction
Dorian Gailhard, Enzo Tartaglione, Lirida Naviner +1
Graph generative models perform well on small-scale structured data but struggle to scale to large, complex structures. Hierarchical approaches improve scalability but often ignore…
HYGENE: A Diffusion-based Hypergraph Generation Method
Dorian Gailhard, Enzo Tartaglione, Lirida Naviner +1
Hypergraphs are powerful mathematical structures that can model complex, high-order relationships in various domains, including social networks, bioinformatics, and recommender sys…
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…
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…