5 citations · 5 across the 4 of their papers we have counts for
5 papers · 1 filter
Sheaf-MPNN: A Library for Sheaf Neural Networks as Message Passing
Alessio Borgi, Mario Severino, Luke Braithwaite +14
Standard Graph Neural Networks (GNNs) implicitly assume that neighboring nodes share the same feature space, which contributes to oversmoothing and poor performance on heterophilic…
Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
Alessio Borgi, Mario Severino, Fabrizio Silvestri +1
Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how vector information can be trans…
Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves
Alessio Borgi, Fabrizio Silvestri, Pietro Liò
Sheaf Neural Networks equip graph structures with a cellular sheaf: a geometric structure which assigns local vector spaces (stalks) and a linear learnable restriction/transport ma…
Heterogeneous Sheaf Neural Networks
Luke Braithwaite, Alessio Borgi, Gabriele Onorato +4
Heterogeneous graphs, whose nodes and edges can belong to different types and feature spaces, arise in many real-world domains, including biology, recommendation, social networks,…
Sheaf Hypergraph Networks
Iulia Duta, Giulia Cassarà, Fabrizio Silvestri +1
Higher-order relations are widespread in nature, with numerous phenomena involving complex interactions that extend beyond simple pairwise connections. As a result, advancements in…