Bootstrapping Exchangeable Random Graphs

Fuente: arXiv
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Main Authors: Green, Alden, Shalizi, Cosma Rohilla
Format: Preprint
Published: 2017
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author Green, Alden
Shalizi, Cosma Rohilla
author_facet Green, Alden
Shalizi, Cosma Rohilla
contents We introduce two new bootstraps for exchangeable random graphs. One, the "empirical graphon bootstrap", is based purely on resampling, while the other, the "histogram bootstrap", is a model-based "sieve" bootstrap. We show that both of them accurately approximate the sampling distributions of motif densities, i.e., of the normalized counts of the number of times fixed subgraphs appear in the network. These densities characterize the distribution of (infinite) exchangeable networks. Our bootstraps therefore give a valid quantification of uncertainty in inferences about fundamental network statistics, and so of parameters identifiable from them.
format Preprint
id arxiv_https___arxiv_org_abs_1711_00813
institution arXiv
publishDate 2017
record_format arxiv
spellingShingle Bootstrapping Exchangeable Random Graphs
Green, Alden
Shalizi, Cosma Rohilla
Methodology
We introduce two new bootstraps for exchangeable random graphs. One, the "empirical graphon bootstrap", is based purely on resampling, while the other, the "histogram bootstrap", is a model-based "sieve" bootstrap. We show that both of them accurately approximate the sampling distributions of motif densities, i.e., of the normalized counts of the number of times fixed subgraphs appear in the network. These densities characterize the distribution of (infinite) exchangeable networks. Our bootstraps therefore give a valid quantification of uncertainty in inferences about fundamental network statistics, and so of parameters identifiable from them.
title Bootstrapping Exchangeable Random Graphs
topic Methodology
url https://arxiv.org/abs/1711.00813