The persistent Laplacian of non-branching complexes

Non-branching matrices are real matrices with entries in $ \{-1, 0, 1\} $, where each row contains at most two non-zero entries. Such matrices naturally arise in the study of Laplacians of pseudomanifolds and cubical complexes. We show that a basis for the kernel of a non-branching matrix can be computed in near-linear time. Specifically, the basis has the special property that the supports of all the column vectors in it are disjoint with each other. Building on this result, we show that the up persistent Laplacian can be computed in near-linear time for a pair of such spaces, and its eigenvalues can be more efficiently computed via computing singular values. In addition to that, we analyze the arithmetic operations of up persistent Laplacian with respect to a non-branching filtration. Furthermore, we show that the up persistent Laplacian of $ q $-non-branching simplicial complexes can be represented as the Laplacian of an associated hypergraph, thus providing a higher-dimensional generalization of the Kron reduction, as well as a Cheeger-type inequality. Finally, we highlight the efficiency of our method on image data.

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Publication Details

Journal
Foundations of Data Science
Published
2025-10-11
DOI
https://doi.org/10.3934/fods.2025020
Primary Topic
Topological and Geometric Data Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

The persistent Laplacian of non-branching complexes

Rui Dong, Magnus Bakke Botnan
Foundations of Data Science
Topological and Geometric Data Analysis
article

The persistent Laplacian of non-branching complexes

Rui Dong, Magnus Bakke Botnan
article en

Abstract

Non-branching matrices are real matrices with entries in $ \{-1, 0, 1\} $, where each row contains at most two non-zero entries. Such matrices naturally arise in the study of Laplacians of pseudomanifolds and cubical complexes. We show that a basis for the kernel of a non-branching matrix can be computed in near-linear time. Specifically, the basis has the special property that the supports of all the column vectors in it are disjoint with each other. Building on this result, we show that the up persistent Laplacian can be computed in near-linear time for a pair of such spaces, and its eigenvalues can be more efficiently computed via computing singular values. In addition to that, we analyze the arithmetic operations of up persistent Laplacian with respect to a non-branching filtration. Furthermore, we show that the up persistent Laplacian of $ q $-non-branching simplicial complexes can be represented as the Laplacian of an associated hypergraph, thus providing a higher-dimensional generalization of the Kron reduction, as well as a Cheeger-type inequality. Finally, we highlight the efficiency of our method on image data.

Foundations of Data ScienceVol. 10(0)
Vrije Universiteit Amsterdam (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 70%
Topological and Geometric Data Analysis
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The persistent Laplacian of non-branching complexes — Rui Dong, Magnus Bakke Botnan · Foundations of Data Science (2025) | TGRS Research Map | TGRS