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Symmetrizing k-nn and Mutual k-nn Smoothers Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Extension Sampling Designs for Big Networks: Application to Twitter

Author : A. Rebecq

Published in: Nonparametric Statistics

Publisher: Springer International Publishing

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Abstract

With the rise of big data, more and more attention is paid to statistical network analysis. However, exact computation of many statistics of interest is of prohibitive cost for big graphs. Statistical estimators can thus be preferable. Model-based estimators for networks have some drawbacks. We study design-based estimates relying on sampling methods that were developed specifically for use on graph populations. In this contribution, we test some sampling designs that can be described as “extension” sampling designs. Unit selection happens in two phases: in the first phase, simple designs such as Bernoulli sampling are used, and in the second phase, some units are selected among those that are somehow linked to the units in the first-phase sample. We test these methods on Twitter data, because the size and structure of the Twitter graph is typical of big social networks for which such methods would be very useful.

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previous chapter Bootstrapping Nonparametric M-Smoothers with Independent Error Terms
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Footnotes
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Metadata
Title
Extension Sampling Designs for Big Networks: Application to Twitter
Author
A. Rebecq
Copyright Year
2018
Book
Nonparametric Statistics

Print ISBN: 978-3-319-96940-4

Electronic ISBN: 978-3-319-96941-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-96941-1_17

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