A Unified Framework of Hyperbolic Graph Representation Learning Methods
Hyperbolic geometry has emerged as an effective latent space for representing complex networks, owing to its ability to capture hierarchical organization and heterogeneous connectivity patterns using low-dimensional embeddings. As a result, numerous hyperbolic graph representation learning methods have been proposed in recent years. However, their practical adoption and systematic comparison remain challenging, as implementations are fragmented and shared tools for reproducible and fair evaluation are lacking. In this work, we introduce a unified open-source framework for hyperbolic graph representation learning that integrates several widely used embedding methods under a common optimiza...
| Authors | Sofía Pérez Casulo, Marcelo Fiori, Bernardo Marenco et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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