双曲几何因其能在低维嵌入中有效刻画网络的层次化结构与异质连通模式，已成为表征复杂网络的一种高效潜在空间。近年来，大量双曲图表示学习方法相继被提出。然而，其实用化部署与系统性比较仍面临挑战：现有实现分散、缺乏支持可复现且公平评估的通用工具。本文提出一个开源的双曲图表示学习统一框架，将若干广泛使用的嵌入方法集成于统一的优化接口之下。该新颖框架支持双曲嵌入的一致化训练、可视化与评估，并可无缝对接标准网络分析工具。依托这一统一设置，我们在真实世界网络上对双曲嵌入方法开展了实验研究，聚焦于两类典型下游任务：链路预测与节点分类。除预测精度外，本研究还提供了关于现有方法优势与局限性的实践性洞见，从而助力研究者进行方法的合理选择，并推动双曲图表示学习领域的可复现研究。

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.