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cs.LG2025
Superposition in Graph Neural Networks
Lukas Pertl, Han Xuanyuan, Pietro Liò
Interpreting graph neural networks (GNNs) is difficult because message passing mixes signals and internal channels rarely align with human concepts. We study superposition, the sha…
cs.LG2025
Wasserstein Hypergraph Neural Network
Iulia Duta, Pietro Liò
The ability to model relational information using machine learning has driven advancements across various domains, from medicine to social science. While graph representation learn…
cs.LG2025
Sheaves Reloaded: A Directional Awakening
Stefano Fiorini, Hakan Aktas, Iulia Duta +4
Sheaf Neural Networks (SNNs) represent a powerful generalization of Graph Neural Networks (GNNs) that significantly improve our ability to model complex relational data. While dire…