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We show mathematically that hydra satisfies a certain optimality guarantee: it minimizes the \u2018hyperbolic strain\u2019 between original and embedded data points. Moreover, it is able to recover points exactly, when they are contained in a low-dimensional hyperbolic subspace 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+, typically outperforms existing methods in both computation time and embedding quality.<\/jats:p>","DOI":"10.1093\/comnet\/cnaa002","type":"journal-article","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T20:08:55Z","timestamp":1577995735000},"source":"Crossref","is-referenced-by-count":31,"title":["Hydra: a method for strain-minimizing hyperbolic embedding of network- and distance-based data"],"prefix":"10.1093","volume":"8","author":[{"given":"Martin","family":"Keller-Ressel","sequence":"first","affiliation":[{"name":"Institute of Mathematical Stochastics, TU Dresden, 01062 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephanie","family":"Nargang","sequence":"additional","affiliation":[{"name":"Institute of Mathematical Stochastics, TU Dresden, 01062 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"2020090706434512400_B1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.is.2003.10.002","article-title":"H-MDS: a new approach for interactive visualization with multidimensional scaling in the hyperbolic space","volume":"29","author":"Walter,","year":"2004","journal-title":"Inf. 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