Efficient Distributed Differential Network Estimation

Differential network is an essential tool to reveal structural changes across two populations. However, existing single-machine methods for estimating differential network face computational and storage limitations when dealing with large-scale data sets. To address this issue, this paper develops a distributed estimation algorithm, which divides the estimation of differential network into several small-scale node-wise regression tasks and reduces local estimation bias through a debiasing technique. After aggregating debiased estimators, a global estimator is constructed efficiently. Theoretical analysis shows that the proposed distributed estimator can achieve global estimation consistency under mild conditions, with a convergence rate comparable to that of the single-machine method, while also facilitating support set recovery. Finally, we provide extensive numerical experiments to demonstrate the superior performance of our estimator compared to several baselines.