Hydra: A method for strain-minimizing hyperbolic embedding of network- and distance-based data
This addresses the need for efficient hyperbolic embeddings in network analysis, though it appears incremental as it builds on existing methods.
The authors tackled the problem of embedding network- or distance-based data into hyperbolic space by introducing Hydra, a method that minimizes hyperbolic strain and recovers points exactly under certain conditions, achieving competitive embedding quality with substantially shorter computation times and outperforming existing methods with Hydra+.
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.