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Related Experiment Video

Updated: Jul 6, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Tracing information flow on a global scale using Internet chain-letter data.

David Liben-Nowell1, Jon Kleinberg

  • 1Department of Computer Science, Carleton College, Northfield, MN 55057, USA. dlibenno@carleton.edu

Proceedings of the National Academy of Sciences of the United States of America
|March 21, 2008
PubMed
Summary
This summary is machine-generated.

Information spreads through social networks in narrow, deep chains, not wide, shallow ones. This study reveals a new model for information propagation, challenging small-world theories.

Related Experiment Videos

Last Updated: Jul 6, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

  • Social Network Analysis
  • Information Propagation Dynamics
  • Computational Social Science

Background:

  • Understanding how information spreads globally is crucial but complex.
  • Existing models often rely on 'small-world' principles, suggesting rapid, wide dissemination.
  • The precise mechanisms of large-scale information spread remain largely uncharacterized.

Purpose of the Study:

  • To investigate the person-by-person mechanics of information spread on a global scale.
  • To reconstruct the propagation pathways of widely circulated Internet chain letters.
  • To develop a new model that accurately reflects observed information diffusion patterns.

Main Methods:

  • Reconstruction of information propagation at an individual level.
  • Analysis of massively circulated Internet chain letters.
  • Development of a probabilistic model incorporating network clustering and asynchronous response times.

Main Results:

  • Information spread, exemplified by chain letters, follows a narrow, deep, tree-like pattern, not a wide, shallow one.
  • Propagation can extend for several hundred steps, contradicting simple 'small-world' network predictions.
  • The observed pattern suggests a more complex information diffusion dynamic within social networks.

Conclusions:

  • The spread of information in social networks is more intricate than previously modeled.
  • A new understanding of information diffusion is needed, considering factors like network clustering and response times.
  • The proposed probabilistic model effectively replicates the observed deep, narrow propagation structures.