基于拓扑的链接预测的内在限制
Inherent Limits on Topology-Based Link Prediction
AI总结：
本文指出图的自同构等属性导致链接预测存在内在限制，计算了仅基于图拓扑的预测上界，发现稀疏图的上界极低，证明此类预测系统必须依赖拓扑之外的额外信息。
摘要：
链接预测系统（如推荐系统）通常将图拓扑作为其主要信息源之一。然而，图的自同构及相关属性导致了可预测性的内在限制。我们计算了在多种真实世界图中，仅依靠图拓扑能在多大程度上实现链接预测的严格上界。我们发现，在最稀疏的图中，这些上界低得令人惊讶，从而证明基于稀疏图数据的预测系统存在内在限制，需要图拓扑之外的信息。
英文摘要：
Link prediction systems (e.g. recommender systems) typically use graph topology as one of their main sources of information. However, automorphisms and related properties of graphs beget inherent limits in predictability. We calculate hard upper bounds on how well graph topology alone enables link prediction for a wide variety of real-world graphs. We find that in the sparsest of these graphs the upper bounds are surprisingly low, thereby demonstrating that prediction systems on sparse graph data are inherently limited and require information in addition to the graph topology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
