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Persistent sheaf cohomology

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arxiv 2204.13446 v1 pith:F5MNETWE submitted 2022-04-28 math.AT

classification math.AT
keywords cohomologymodulespersistencealgebraicconstructionsdimensionsheaftopological
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We expand the toolbox of (co)homological methods in computational topology by applying the concept of persistence to sheaf cohomology. Since sheaves (of modules) combine topological information with algebraic information, they allow for variation along an algebraic dimension and along a topological dimension. Consequently, we introduce two different constructions of sheaf cohomology (co)persistence modules. One of them can be viewed as a natural generalization of the construction of simplicial or singular cohomology copersistence modules. We discuss how both constructions relate to each other and show that, in some cases, we can reduce one of them to the other. Moreover, we show that we can combine both constructions to obtain two-dimensional (co)persistence modules with a topological and an algebraic dimension. We also show that some classical results and methods from persistence theory can be generalized to sheaves. Our results open up a new perspective on persistent cohomology of filtrations of simplicial complexes.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cellular sheaf Laplacians on the set of simplices of symmetric simplicial set induced by hypergraph

    math.AT 2024-11 conditional novelty 6.0 of 10

    Cellular sheaf Laplacians are generalized to symmetric simplicial sets induced by hypergraphs, with a Hodge theorem connecting their kernels to sheaf cohomology.

  2. Persistent Sheaf Laplacian Analysis of Protein Flexibility

    q-bio.BM 2025-02 reject novelty 4.0 of 10

    A protein flexibility predictor based on persistent sheaf Laplacians claims a 32% improvement over GNM, but the gain is an artifact of in-sample regression and disappears in the authors' own blind tests.

  3. Topological Data Analysis and Topological Deep Learning Beyond Persistent Homology -- A Review

    math.HO 2025-07 conditional novelty 3.0 of 10

    A survey organizing recent TDA and TDL methods beyond persistent homology and connecting them to data structures and vectorization.

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