  This paper focuses on the bootstrap for network dependent processes under the
conditional $\psi$-weak dependence. Such processes are distinct from other
forms of random fields studied in the statistics and econometrics literature so
that the existing bootstrap methods cannot be applied directly. We propose a
block-based approach and a modification of the dependent wild bootstrap for
constructing confidence sets for the mean of a network dependent process. In
addition, we establish the consistency of these methods for the smooth function
model and provide the bootstrap alternatives to the network
heteroskedasticity-autocorrelation consistent (HAC) variance estimator. We find
that the modified dependent wild bootstrap and the corresponding variance
estimator are consistent under weaker conditions relative to the block-based
method, which makes the former approach preferable for practical
implementation.
