Using selection bias to explain the observed structure of Internet diffusions

Benjamin Golub*, Matthew O. Jackson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Recently, large datasets stored on the Internet have enabled the analysis of processes, such as large-scale diffusions of information, at new levels of detail. In a recent study, Liben-Nowell and Kleinberg [(2008) Proc Natl Acad Sci USA 105:4633-4638] observed that the flow of information on the Internet exhibits surprising patterns whereby a chain letter reaches its typical recipient through long paths of hundreds of intermediaries. We show that a basic Galton-Watson epidemic model combined with the selection bias of observing only large diffusions suffices to explain these patterns. Thus, selection biases of which data we observe can radically change the estimation of classical diffusion processes.

Original languageEnglish (US)
Pages (from-to)10833-10836
Number of pages4
JournalProceedings of the National Academy of Sciences of the United States of America
Volume107
Issue number24
DOIs
StatePublished - Jun 15 2010
Externally publishedYes

Keywords

  • Chain letters
  • Diameter
  • Galton-Watson process
  • Maximum likelihood estimation
  • Social networks

ASJC Scopus subject areas

  • General

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