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Extension Sampling Designs for Big Networks: Application to Twitter

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Nonparametric Statistics (ISNPS 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 250))

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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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Notes

  1. 1.

    https://dev.twitter.com/streaming/overview.

  2. 2.

    https://dev.twitter.com/rest/public.

  3. 3.

    http://reverb.guru/view/597295533668271595.

  4. 4.

    https://support.twitter.com/articles/119135.

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Author information

Authors and Affiliations

  1. Modal’X, UPL, University Paris Nanterre, Nanterre, France

    A. Rebecq

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  1. A. Rebecq
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Correspondence to A. Rebecq .

Editor information

Editors and Affiliations

  1. MODAL’X, Paris West University Nanterre La Défense, Nanterre, France

    Patrice Bertail

  2. LMA, Avignon University, Avignon, France

    Delphine Blanke

  3. MIASHS, University of Rennes 2, Rennes, France

    Pierre-André Cornillon

  4. Formation Continue CEPE, Ecole Nationale de la Statistique et de l’Administration, Malakoff, France

    Eric Matzner-Løber

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Rebecq, A. (2018). Extension Sampling Designs for Big Networks: Application to Twitter. In: Bertail, P., Blanke, D., Cornillon, PA., Matzner-Løber, E. (eds) Nonparametric Statistics. ISNPS 2016. Springer Proceedings in Mathematics & Statistics, vol 250. Springer, Cham. https://doi.org/10.1007/978-3-319-96941-1_17

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