Challenges in the inference on random networks relate to the restrictive and hard to validate parametric assumptions, as well as data volume and velocity, which deprive one of the ability to obtain population parameters directly. Sampling procedures, coupled with nonparametric bootstrap, circumvent the problems of parametric model specification and incomplete information about the network. The proposed nonparametric patchwork resampling adapts the "blocking" argument, developed for time series bootstrap and spatial data re-tiling, to random networks. In contrast to block bootstrap in time series, its primary focus is on mirroring the asymptotic distribution of certain statistics of interest rather than on recreating the data generating process. In this presentation, we focus on how the new bootstrap procedure can be used to quantify estimation uncertainty for network statistics that are functions of degree distribution. We present a new computationally efficient and data-driven cross-validation algorithm for selecting an optimal patch size. The suggested procedures are illustrated using simulated and observed social networks.