对比13种超球图嵌入方法，找出最适合链接预测与拓扑重建的方案。
Hyperbolic Graph Embedders for Link Prediction and Topology Reconstruction
- 统一协议下测试13种无监督超球嵌入方法。
- 最大似然与表征学习方法在多数任务中表现最优。
- 提供按网络结构选择方法的实用指南。
超球嵌入能以紧凑的几何表示复杂网络，但机器学习、网络科学和算法领域发展的方法系统性对比仍罕见。我们在合成与真实网络上，采用统一协议对13种无监督超球图嵌入方法进行链路预测与拓扑重构评估。该协议同时考量缺失链路恢复与局部及全局结构保留能力。最大似然与基于表征学习的方法（含混合变体）整体表现最强，但无单一方法在所有任务与结构范式中占优。性能更依赖于嵌入范式而非学科来源。我们识别出不同范式在各类网络结构中的成败场景，并为下游应用提供方法选择建议。
原文摘要 · Abstract (English)
Hyperbolic embeddings provide compact geometric representations of complex networks in hyperbolic spaces, but systematic comparisons of methods developed in machine learning, network science, and algorithmics remain rare. We benchmark 13 unsupervised hyperbolic graph embedders under a unified protocol for link prediction and topology reconstruction on synthetic and empirical networks. The protocol captures both missing-link recovery and the preservation of local and global network structure. Maximum-likelihood and representation-learning-based approaches, including hybrid variants, achieve the strongest overall performance, although no method dominates across all tasks and structural regimes. Performance is more strongly associated with embedding paradigm than with disciplinary origin. We identify the network regimes in which different paradigms succeed or fail and provide practical guidance for method selection in downstream applications.
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