A0971
Title: Network bootstrap using overlapping partitions
Authors: Sayan Chakrabarty - University of Michigan (United States) [presenting]
Liza Levina - University of Michigan (United States)
Abstract: Bootstrapping network data efficiently is a challenging task. The existing methods tend to make strong assumptions on both the network structure and the statistics being bootstrapped, and are computationally costly. The aim is to introduce a general algorithm, SSBoot, for network bootstrap that partitions the network into multiple overlapping subnetworks and then aggregates results from bootstrapping these subnetworks to generate a bootstrap sample of the network statistic of interest. This approach tends to be much faster than competing methods, as most of the computations are done on smaller subnetworks. It is shown that SSBoot is consistent in distribution for a large class of network statistics under minimal assumptions on the network structure, and it is demonstrated with extensive numerical examples that the bootstrap confidence intervals produced by SSBoot attain good coverage without substantially increasing interval lengths in a fraction of the time needed for running competing methods.
