4 papers
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…
FAROS: Fair Graph Generation via Attribute Switching Mechanisms
Abdennacer Badaoui, Oussama Kharouiche, Hatim Mrabet +2
Recent advancements in graph diffusion models (GDMs) have enabled the synthesis of realistic network structures, yet ensuring fairness in the generated data remains a critical chal…
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato +3
Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adve…
Uplift Modeling Under Limited Supervision
George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros +1
Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such…