We introduce hydra (hyperbolic distance recovery and approximation), a new method for embedding network- or distance-based data into hyperbolic space. We show mathematically that hydra satisfies a certain optimality guarantee: It minimizes the `hyperbolic strain' between original and embedded data points. Moreover, it recovers points exactly, when they are located on a hyperbolic submanifold of the feature space. Testing on real network data we show that the embedding quality of hydra is competitive with existing hyperbolic embedding methods, but achieved at substantially shorter computation time. An extended method, termed hydra+, outperforms existing methods in both computation time and embedding quality.
Cite
@article{arxiv.1903.08977,
title = {Hydra: A method for strain-minimizing hyperbolic embedding of network- and distance-based data},
author = {Martin Keller-Ressel and Stephanie Nargang},
journal= {arXiv preprint arXiv:1903.08977},
year = {2019}
}