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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
Recently, large datasets stored on the Internet have enabled the analysis of processes, such as large-scale 擴散s of 資訊, 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 資訊 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 流行病學c model combined with the selection bias of observing only large 擴散s suffices to explain these patterns. Thus, selection biases of which data we observe can radically change the estimation of classical 擴散 processes.
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