Topological Deep Learning: Graphs, Complexes, Sheaves
The types of spaces where data resides - graphs, meshes, grids, manifolds - are becoming increasingly varied and heterogeneous. Therefore, translating ideas, models, and theoretical results between different domains is becoming more and more challenging. Nonetheless, two fundamental principles unite all these settings. The first states that data is localised, meaning that data is associated with some regions of the…