Presented by:
Yulia Gel
Date:
Wednesday 7th December 2016 - 12:00 to 12:30
Venue:
INI Seminar Room 1
Abstract:
We propose a new method of nonparametric bootstrap to quantify
estimation uncertainties in functions of network degree distribution in large
ultra sparse networks. Both network degree distribution and network order are
assumed to be unknown. The key idea is based on adaptation of the ``blocking''
argument, developed for bootstrapping of time series and re-tiling of spatial
data, to random networks. We first sample a set of multiple ego networks of
varying orders that form a patch, or a network block analogue, and then
resample the data within patches. To select an optimal patch size, we develop a
new computationally efficient and data-driven cross-validation algorithm. In
our simulation study, we show that the new fast patchwork bootstrap (FPB)
outperforms competing approaches by providing sharper and better calibrated
confidence intervals for functions of a network degree distribution, including
the cases of networks in an ultra sparse regime. We illustrate the FPB
in application to analysis of social networks and discuss its potential utility
for nonparametric anomaly detection and privacy-preserving data mining.