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Temporal scaling in information propagation.

Junming Huang1, Chao Li2, Wen-Qiang Wang3

  • 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China.

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Summary
This summary is machine-generated.

Information propagation probability decreases over time since the last interaction, following a power-law. This finding improves prediction accuracy on social networks and enhances viral marketing effectiveness.

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Area of Science:

  • Social Network Analysis
  • Information Dynamics
  • Computational Social Science

Background:

  • Understanding information propagation is key to analyzing social networks.
  • Existing models often overlook the temporal dynamics of individual interactions.
  • This gap leads to inaccurate predictions on evolving social networks.

Purpose of the Study:

  • To investigate the temporal laws governing individual interactions in information propagation.
  • To develop a model that accounts for the time-decay of interaction influence.
  • To improve the accuracy of predicting information spread on social media.

Main Methods:

  • Empirical analysis of information propagation data from a large-scale social media platform.
  • Identification of a temporal scaling law in message propagation probability.
  • Development and validation of a temporal model for propagation prediction.

Main Results:

  • Discovered a power-law relationship between propagation probability and time latency since the last interaction.
  • The proposed temporal model significantly reduced prediction error rates from 6.7% to 2.6%.
  • Demonstrated a 9.7% increase in incremental customers through improved viral marketing.

Conclusions:

  • Individual interaction dynamics exhibit a predictable temporal decay, crucial for information propagation models.
  • Accounting for interaction latency enhances the accuracy of predicting information spread on social networks.
  • The findings have practical implications for optimizing viral marketing strategies.