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Barbara Mahler: Contagion Dynamics for Manifold Learning

Time: Tue 2022-01-18 10.15

Location: Zoom (meeting ID: 659 3743 5667)

Video link: Meeting ID: 659 3743 5667

Participating: Barbara Mahler (KTH)

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. In this talk, I will present how contagion maps and variants thereof perform as a manifold-learning tool on a number of different synthetic and real-world data sets, and compare their performance to that of Isomap, one of the most well-known manifold-learning algorithms. I will show 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.