We propose a novel method of network detection that is robust against any complex dependence structure. Our goal is to conduct exploratory network detection, meaning that we attempt to detect a network composed of ``connectable'' edges that are worth investigating in detail for further modelling or precise network analysis. For a reproducible network detection, we pursuit high power while controlling the false discovery rate (FDR). In particular, we formalize the problem as a multiple testing, and propose p-variables that are used in the Benjamini-Hochberg procedure. We show that the proposed method controls the FDR under arbitrary dependence structure with any sample size, and has asymptotic power one. The validity is also confirmed by simulations and a real data example.

翻译：我们提出了一种新颖的网络检测方法，该方法对任何复杂的依赖结构都具有鲁棒性。我们的目标是进行探索性网络检测，即尝试检测出由“可连接”边构成的网络，这些边值得进一步建模或精确网络分析以进行详细研究。为了实现可复现的网络检测，我们在控制错误发现率（FDR）的同时追求高检测功效。具体而言，我们将该问题形式化为多重假设检验，并提出了用于Benjamini-Hochberg程序的p变量。我们证明了所提方法能在任意依赖结构和任意样本量下控制FDR，并具有渐近功效为一的性质。其有效性也通过仿真和真实数据案例得到了验证。