Structure and Stability of the 1-Dimensional Mapper
Not on the map. Why?
Abstract (arXiv)
Given a continuous function f:X→ℝ and a cover I of its image by intervals, the Mapper is the nerve of a refinement of the pullback cover f-1(I). Despite its success in applications, little is known about the structure and stability of this construction from a theoretical point of view. As a pixelized version of the Reeb graph of f, it is expected to capture a subset of its features (branches, holes), depending on how the interval cover is positioned with respect to the critical values of the function. Its stability should also depend on this positioning. We propose a theoretical framework that relates the structure of the Mapper to the one of the Reeb graph, making it possible to predict which features will be present and which will be absent in the Mapper given the function and the cover, and for each feature, to quantify its degree of (in-)stability. Using this framework, we can derive guarantees on the structure of the Mapper, on its stability, and on its convergence to the Reeb graph as the granularity of the cover I goes to zero.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 0; 2026: 2.
6 papers.
- 2021A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data Analysiscited by 5
- 2023Statistical learning on measures: an application to persistence diagramscited by 3
- 2024A distribution-guided Mapper algorithmcited by 2
- 2024A Mapper Algorithm with implicit intervals and its optimizationcited by 0
- 2026Structure-First Point Cloud Learning with Mapper Region Graphscited by 0
- 2026Skeletal Prototypes on Iterative Nerve Expansionscited by 0
- 2026Skeletal Prototypes on Iterative Nerve Expansions14 Sep 2026
- 2026Structure-First Point Cloud Learning with Mapper Region Graphs5 Feb 2026
- 2024A Mapper Algorithm with implicit intervals and its optimization16 Dec 2024
- 2024A distribution-guided Mapper algorithm19 Jan 2024
- 2023Statistical learning on measures: an application to persistence diagrams15 Mar 2023
- 2021A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data Analysis1 Sep 2021