# 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.