  The increasing availability of data has generated unprecedented prospects for
network analyses in many biological fields, such as neuroscience (e.g., brain
networks), genomics (e.g., gene-gene interaction networks), and ecology (e.g.,
species interaction networks). A powerful statistical framework for estimating
such networks is Gaussian graphical models, but standard estimators for the
corresponding graphs are prone to large numbers of false discoveries. In this
paper, we introduce a novel graph estimator based on knockoffs that imitate the
partial correlation structures of unconnected nodes. We show that this new
estimator guarantees accurate control of the false discovery rate in theory,
simulations, and biological applications, and we provide easy-to-use R code.
