用于错误发现率控制的保形链接预测
Conformal link prediction for false discovery rate control
AI总结：
针对图链接预测中缺失边难以直接分类为真实边或不存在边的问题，提出基于保形推断的新方法以控制错误发现率，并考虑图结构引起的复杂数据依赖性，在模拟与真实数据上均验证了有效性。
摘要：
大多数链接预测方法返回图中缺失边连接概率的估计。此类输出可用于将缺失边从最可能为真到最不可能为真进行排序，但无法直接将其分类为真实边和不存在边。在本研究中，我们考虑在控制错误发现率（FDR）的情况下识别一组真实边的问题。我们提出了一种基于保形推断文献中高层思想的新颖方法。图结构在数据中引起了复杂的依赖关系，我们对此进行了仔细考虑，因为这使得该设置不同于假设数据可交换性的常规保形推断设置。FDR控制在模拟数据和真实数据上均得到了实证验证。
英文摘要：
Most link prediction methods return estimates of the connection probability of missing edges in a graph. Such output can be used to rank the missing edges from most to least likely to be a true edge, but does not directly provide a classification into true and non-existent. In this work, we consider the problem of identifying a set of true edges with a control of the false discovery rate (FDR). We propose a novel method based on high-level ideas from the literature on conformal inference. The graph structure induces intricate dependence in the data, which we carefully take into account, as this makes the setup different from the usual setup in conformal inference, where data exchangeability is assumed. The FDR control is empirically demonstrated for both simulated and real data.
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。
