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Updated: Sep 23, 2025

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Contagion Dynamics for Manifold Learning.

Barbara I Mahler1

  • 1Mathematical Institute, University of Oxford, Oxford, United Kingdom.

Frontiers in Big Data
|May 16, 2022
PubMed
Summary
This summary is machine-generated.

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Articles linked to this work by shared authors, journal, and citation graph.

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Same author

A statistical approach to knot confinement via persistent homology.

Proceedings. Mathematical, physical, and engineering sciences·2022
See all related articles

Contagion maps effectively reveal network structures in noisy data, outperforming traditional manifold learning methods like Isomap. Pre-processing distance estimates further enhances results for both techniques.

Area of Science:

  • Network Science
  • Data Analysis
  • Machine Learning

Background:

  • Contagion maps utilize activation times in threshold contagions to map network nodes to vectors in high-dimensional space.
  • The resulting point cloud reflects both network structure and contagion spread dynamics.
  • This suggests contagion maps as a potential manifold learning technique.

Purpose of the Study:

  • To evaluate contagion maps and their variants as manifold learning tools.
  • To compare their performance against established algorithms like Isomap.
  • To investigate the impact of pre-processing distance estimates on manifold learning results.

Main Methods:

  • Application of contagion maps to synthetic and real-world datasets.
  • Comparative analysis with the Isomap algorithm.
Keywords:
contagiondimensionality reductionmanifold learningpersistent homologytopological data analysis

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  • Exploration of pre-processing distance estimates before manifold learning.

    • Main Results:

    • Contagion maps reliably detect underlying manifold structure in noisy datasets.
    • Contagion maps show superior performance compared to Isomap under noisy conditions.
    • Pre-processing distance estimates improved clarity for both contagion maps and Isomap.

    Conclusions:

    • Contagion maps are validated as a robust manifold learning technique.
    • The method offers advantages in handling noisy network data.
    • Data pre-processing is a crucial step for enhancing manifold learning outcomes.