@article{mahler_contagion_2020,
abstract = {Contagion maps exploit activation times in threshold contagions to assign vectors in high-dimensional Euclidean space to the nodes of a network. A point cloud that is the image of a contagion map reflects both the structure underlying the network and the spreading behaviour of the contagion on it. Intuitively, such a point cloud exhibits features of the network's underlying structure if the contagion spreads along that structure, an observation which suggests contagion maps as a viable manifold-learning technique. We test contagion maps as a manifold-learning tool on a number of different real-world and synthetic data sets, and we compare their performance to that of Isomap, one of the most well-known manifold-learning algorithms. We find that, under certain conditions, contagion maps are able to reliably detect underlying manifold structure in noisy data, while Isomap fails due to noise-induced error. This consolidates contagion maps as a technique for manifold learning.},
author = {Mahler, Barbara I.},
date = {2020-11-30},
eprint = {2012.00091},
eprinttype = {arxiv},
journaltitle = {{arXiv}:2012.00091 [cs, math, stat]},
keywords = {1 - Contagion dynamics, 1 - Machine learning, 2 - Isomap, 2 - Multidimensional scaling, 2 - Persistent homology, 3 - Point cloud},
title = {Contagion Dynamics for Manifold Learning},
url = {http://arxiv.org/abs/2012.00091},
urldate = {2020-12-02}
}