A Unified Framework of Hyperbolic Graph Representation Learning Methods

TL;DR

This paper introduces an open-source unified framework for hyperbolic graph embeddings, enabling consistent evaluation and comparison of methods on real-world network tasks like link prediction and node classification.

Contribution

It provides a common platform that integrates multiple hyperbolic embedding methods, simplifying training, visualization, and evaluation for reproducible research.

Findings

The framework allows fair comparison of hyperbolic embedding methods.

Experimental results reveal strengths and limitations of existing approaches.

The study offers practical insights for method selection in real-world networks.

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.