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SUMMARY:Bootstrapped Inference for Degree Distributions in Large Sparse Ne
 tworks - Yulia Gel (University of Texas at Dallas\; University of Waterloo
 )
DTSTART:20161207T120000Z
DTEND:20161207T123000Z
UID:TALK69368@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:We propose a new method of nonparametric bootstrap to quantify
  estimation uncertainties in functions of network degree distribution in l
 arge ultra sparse networks. Both network degree distribution and network o
 rder are assumed to be unknown. The key idea is based on adaptation of the
  ``blocking&#39\;&#39\; argument\, developed for bootstrapping of time ser
 ies 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 ne
 twork block analogue\, and then resample the data within patches. To selec
 t an optimal patch size\, we develop a new computationally efficient and d
 ata-driven cross-validation algorithm. In our simulation study\, we show t
 hat the new fast patchwork bootstrap (FPB) outperforms competing approache
 s by providing sharper and better calibrated confidence intervals for func
 tions of a network degree distribution\, including the cases of networks i
 n an ultra sparse regime. We illustrate the FPB in application to analysis
  of social networks and discuss its potential utility for nonparametric an
 omaly detection and privacy-preserving data mining.
LOCATION:Seminar Room 1\, Newton Institute
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