A Unified Framework of Hyperbolic Graph Representation Learning Methods

2026-04-30

AI Summary

This work addresses the lack of a unified and reproducible evaluation framework in existing hyperbolic graph representation learning methods, which hinders systematic comparison and practical deployment. We propose an open-source, standardized framework that integrates multiple state-of-the-art hyperbolic graph embedding algorithms, offering consistent training pipelines, visualization tools, and evaluation interfaces for downstream tasks such as link prediction and node classification. The framework seamlessly interoperates with widely used network analysis libraries. Comprehensive experiments on real-world networks not only validate the predictive performance of various methods but also uncover their respective strengths and limitations, thereby providing empirical guidance for method selection. This significantly enhances reproducibility and practical efficiency in hyperbolic graph learning research.

Abstract

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 optimization interface. The novel framework enables consistent training, visualization, and evaluation of hyperbolic embeddings, and interfaces seamlessly with standard network analysis tools. Leveraging this unified setup, we conduct an experimental study of hyperbolic embedding methods on real-world networks, focusing on two canonical downstream tasks: link prediction and node classification. Beyond predictive accuracy, the study offers practical insights into the strengths and limitations of existing approaches, thereby facilitating informed method selection and fostering reproducible research in hyperbolic graph representation learning.

Problem

• hyperbolic graph representation learning
• unified framework
• reproducible evaluation
• method comparison
• open-source tools

Innovation

• hyperbolic geometry
• graph representation learning
• unified framework
• reproducible evaluation
• network embedding