Hyperbolic embeddings can represent complex hierarchical networks in low dimensions, but practitioners now have a standardized framework to fairly compare methods and understand their trade-offs before choosing one for their application.
This paper presents a unified framework for hyperbolic graph embedding methods—techniques that represent networks in hyperbolic space to capture hierarchical structures efficiently. The framework consolidates multiple embedding approaches under one interface, enabling fair comparison and reproducible evaluation on real-world networks for tasks like link prediction and node classification.