Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data
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Abstract (arXiv)
The mapper algorithm is a popular tool from topological data analysis for extracting topological summaries of high-dimensional datasets. In this paper, we present Mapper Interactive, a web-based framework for the interactive analysis and visualization of high-dimensional point cloud data. It implements the mapper algorithm in an interactive, scalable, and easily extendable way, thus supporting practical data analysis. In particular, its command-line API can compute mapper graphs for 1 million points of 256 dimensions in about 3 minutes (4 times faster than the vanilla implementation). Its visual interface allows on-the-fly computation and manipulation of the mapper graph based on user-specified parameters and supports the addition of new analysis modules with a few lines of code. Mapper Interactive makes the mapper algorithm accessible to nonspecialists and accelerates topological analytics workflows.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 2; 2026: 1.
7 papers.
- 2022Experimental Observations of the Topology of Convolutional Neural Network Activationscited by 10
- 2025Explainable Mapper: Charting LLM Embedding Spaces Using Perturbation-Based Explanation and Verification Agentscited by 4
- 2023G-Mapper: Learning a Cover in the Mapper Constructioncited by 2
- 2025Visual Exploration of Feature Relationships in Sparse Autoencoders with Curated Conceptscited by 2
- 2023Topological Data Mapping of Online Hate Speech, Misinformation, and General Mental Health: A Large Language Model Based Studycited by 1
- 2024Bi-Filtration and Stability of TDA Mapper for Point Cloud Datacited by 0
- 2026TopoAlign: Topology-Aware Visual Representation Alignmentcited by 0
- 2026TopoAlign: Topology-Aware Visual Representation Alignment25 May 2026
- 2025Visual Exploration of Feature Relationships in Sparse Autoencoders with Curated Concepts8 Nov 2025
- 2025Explainable Mapper: Charting LLM Embedding Spaces Using Perturbation-Based Explanation and Verification Agents24 Jul 2025
- 2024Bi-Filtration and Stability of TDA Mapper for Point Cloud Data25 Sep 2024
- 2023Topological Data Mapping of Online Hate Speech, Misinformation, and General Mental Health: A Large Language Model Based Study22 Sep 2023
- 2023G-Mapper: Learning a Cover in the Mapper Construction12 Sep 2023
- 2022Experimental Observations of the Topology of Convolutional Neural Network Activations1 Dec 2022