用于链接预测与拓扑重构的双曲图嵌入器

中文摘要 AI 辅助

双曲嵌入能在双曲空间中为复杂网络提供紧凑的几何表示，但机器学习、网络科学与算法学领域所开发方法的系统性对比仍较为罕见。我们在统一协议下对13种无监督双曲图嵌入器进行基准测试，测试任务为合成网络与实证网络上的链接预测及拓扑重构，该协议涵盖缺失链接恢复以及局部与全局网络结构的保留。基于最大似然法与表示学习的方法（含混合变体）整体性能最强，不过没有任何方法能在所有任务与结构 regime 中占据主导。性能与嵌入范式的关联度高于其与学科起源的关联度。我们明确了不同范式成功或失败的网络 regime，并为下游应用中的方法选择提供实用指导。

英文摘要

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.

发表机构

• TU Delft（代尔夫特理工大学）
• Indiana University（印第安纳大学）
• Institute of Informatics, University of Warsaw（华沙大学信息学院）

机构由 AI 辅助整理，请以论文原文为准。