Using the bootstrap for statistical inference on random graphs

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  • Author: Mary E. Thompson, Lilia L. Ramirez Ramirez, Vyacheslav Lyubchich and Yulia R. Gel
  • Date: 13 November 2015
  • Copyright: Image appears courtesy of Getty Images

An article has just published in the Canadian Journal of Statistics on using the bootstrap for statistical inference on random graphs. Online social media networks such as Facebook, LinkedIn and Twitter provide a wealth of new information and ignite interest in developing novel methods for analysis of such newly available data.

The problem is that conclusions of social media network studies often arrive without any reliable estimation of uncertainties or errors, thus leading to a question of the credibility of such results. In this paper, we propose a new data-driven method to quantify uncertainties in estimation of network mean degree, which is the average number of network connections each individual has. With a pre-defined level of confidence, this method can help researchers to find out, for example, in which country teenagers have more Facebook friends on average, or how comparable the efforts of an average LinkedIn employer and LinkedIn job seeker are in terms of the number of contacts they initialize to fill a position.

thumbnail image: Using the bootstrap for statistical inference on random graphs

Using the bootstrap for statistical inference on random graphs

Mary E. Thompson, Lilia L. Ramirez Ramirez, Vyacheslav Lyubchich and Yulia R. Gel

Canadian Journal of Statistics, Early View

DOI: 10.1002/cjs.11271

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