Network knockoffs: controlling false discovery in dyadic space

Abstract

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.