1 citations · 2 across the 5 of their papers we have counts for
8 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…
On the Token Collapse Phenomenon in Textual Graph Soft Prompting
Andrea Giuseppe Di Francesco, Rishabh Jain, Franco Maria Nardini +3
Recent methods for injecting graph structure into LLM prompts include graph soft prompting, in which a graph neural network (GNN) learns graph-specific tokens that capture relation…
Oscillatory Neural Dynamics over Sheaves
Jan-Willem Van Looy, Alessandro Trenta, Alessio Gravina +5
Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that distant nodes effectively i…
Let the Heads Talk: Beyond Diagonal Graph Attention
Riccardo Ali, Alessio Borgi, Mario Severino +4
Sheaf Neural Networks generalize scalar-weighted message passing by replacing scalar edge weights with linear transport maps between local feature spaces. Yet the role of this matr…
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…
Symmetry and Generalisation in Neural Approximations of Renormalisation Transformations
Cassidy Ashworth, Pietro Liò, Francesco Caso
Deep learning models have proven enormously successful at using multiple layers of representation to learn relevant features of structured data. Encoding physical symmetries into t…
Renormalized Graph Representations for Node Classification
Francesco Caso, Giovanni Trappolini, Andrea Bacciu +2
Graph neural networks process information on graphs represented at a given resolution scale. We analyze the effect of using different coarse-grained graph resolutions, obtained thr…