Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 107, Issue 24, Pages 10833-10836Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1000814107
Keywords
diameter; chain letters; Galton-Watson process; maximum likelihood estimation; social networks
Categories
Funding
- National Science Foundation [SES-0647867]
- Jaedicke fellowship at the Stanford Graduate School of Business
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [0961481] Funding Source: National Science Foundation
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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.
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