1 citations · 1 across the 3 of their papers we have counts for
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From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning
Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria Fodor
The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including sc…
Do Sheaf Neural Networks Use Holonomy? A Measure--Intervene--Control Study
Ankit Grover, Rémi Bourgerie
Geometric architectures are often motivated by internal mechanisms, but accuracy alone does not show whether predictions use them. In Sheaf Neural Networks (SNNs), edge transports…
Deep Neural Sheaf Diffusion
Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria Fodor
Deep Graph Neural Networks (GNNs) are essential for capturing complex dependencies in graph-structured data. However, scaling GNNs to depth remains challenging, as stacking layers…
Is One Token All It Takes? Graph Pooling Tokens for LLM-based GraphQA
Ankit Grover, Lodovico Giaretta, Rémi Bourgerie +1
The integration of Graph Neural Networks (GNNs) with Large Language Models (LLMs) has emerged as a promising paradigm for Graph Question Answering (GraphQA). However, effective met…
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks
R. Bourgerie, T. Zanouda
5G and Beyond Networks become increasingly complex and heterogeneous, with diversified and high requirements from a wide variety of emerging applications. The complexity and divers…