Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
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 transformed as it moves across a graph. We introduce \textsc{ESNN}, an Equivariant Sheaf Neural Network that enriches this interaction by learning directed, matrix-valued transport between neighboring vector features while preserving exact Euclidean equivariance. Rather than increasing the order of the representation, ESNN keeps scalar and vector features first-order and places the additional geometric flexibility in the edge transport itself. We characterize this transport theoretically, showing that when relative d...
| Authors | Alessio Borgi, Mario Severino, Fabrizio Silvestri et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 August 2026 |
| Last updated at source | 1 October 2026 |
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