Phenomena such as epidemiological processes, hydrologic systems, social platforms, utility services, and supply chains can be represented as topological networks. A central question about these networks concerns connectivity and the permeability of edges. Dyadic regression and related approaches have been proposed to identify network features associated with pairwise node-level differences. In high-dimensional settings, it is important to control the number of spuriously selected features. However, controlling the false discovery rate for dyadic outcomes is challenging because dependence among dyads invalidates classic asymptotic procedures and complicates standard data splitting and knockoff approaches. We propose a novel knockoff variable selection procedure that simulates synthetic features directly on the topological network prior to constructing the augmented design matrix in dyadic space. Empirically, our method controls the false discovery rate for both node- and edge-level features. The Benjamini-Hochberg, Benjamini-Yekutieli, Storey Q-value, data-splitting, and standard knockoff procedures were all anticonservative. We applied our network knockoffs to assess the impassability of over 1000 stream barriers in North Carolina for Salvelinus fontinalis. Compared to data splitting and traditional knockoff approaches, our proposed approach selected a higher proportion of barriers previously assessed to impede fish movement.

翻译：流行病过程、水文系统、社交平台、公共服务及供应链等现象可表示为拓扑网络。关于这些网络的核心问题涉及连通性与边的渗透性。二元回归及相关方法已被提出用于识别与节点对层级差异相关的网络特征。在高维场景下，控制虚假选择的特征数量至关重要。然而，控制二元结果中的错误发现率具有挑战性，因为二元组间的依赖关系使经典渐近方法失效，并复杂化了标准数据分割和敲除方法。我们提出了一种新型敲除变量选择程序，该方法在构建二元空间扩展设计矩阵之前，直接在拓扑网络上模拟合成特征。实验表明，我们的方法能够有效控制节点级和边级特征的错误发现率。而Benjamini-Hochberg、Benjamini-Yekutieli、Storey Q值、数据分割及标准敲除程序均呈现反保守性。我们将网络敲除应用于评估北卡罗来纳州超过1000个溪流屏障对溪红点鲑通过性的影响。相较于数据分割和传统敲除方法，我们提出的方法筛选出更高比例的被预先评估为阻碍鱼类迁移的屏障。