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Related Experiment Video

Updated: Jun 12, 2026

The Diffusion of Passive Tracers in Laminar Shear Flow

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

Using selection bias to explain the observed structure of Internet diffusions.

Benjamin Golub¹, Matthew O Jackson

¹Graduate School of Business, Stanford University, Stanford, CA 94305, USA. bgolub@stanford.edu

Proceedings of the National Academy of Sciences of the United States of America

|June 11, 2010

Summary

This summary is machine-generated.

Large internet datasets reveal information diffusion patterns. A simple epidemic model explains observed long paths, highlighting how data selection biases affect diffusion analysis.

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Area of Science:

Information Science
Network Analysis
Computational Social Science

Background:

Large-scale internet datasets enable detailed analysis of information diffusion.
Previous studies observed surprisingly long information paths on the internet.

Purpose of the Study:

To explain the observed long information diffusion paths on the internet.
To investigate the impact of selection bias on diffusion process estimation.

Main Methods:

Utilized a basic Galton-Watson epidemic model.
Incorporated selection bias from observing only large diffusion events.

Main Results:

The Galton-Watson model with selection bias successfully replicates the long diffusion paths.
Selection bias significantly alters the estimation of classical diffusion processes.

Conclusions:

Observed internet information diffusion patterns can be explained by simple models with selection bias.
The way data is selected for observation critically influences our understanding of diffusion dynamics.