Sheaf Neural Networks
Overview
Sheaf neural networks study how to learn over graphs when each node has its own feature space and edges specify how neighboring features should be compared or transported. Common approaches build sheaf Laplacians for diffusion, learn matrix-valued restriction maps, and combine them with attention, recurrent or implicit layers, spectral methods, and geometric or temporal models. The framework is being applied to heterophilic graphs, spatio-temporal data, physical systems, and manifold-valued features, with work analyzing expressivity, convergence, oversmoothing, and information bottlenecks. A central open question is when learned sheaf structure adds reliable value over simpler graph models: results depend on architecture and task, so benchmarks and controlled comparisons are important.
Latest papers
29 papers6 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.