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Predicting the evolution of spreading on complex networks.

Duan-Bing Chen1, Rui Xiao2, An Zeng3

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Predicting network spreading is crucial for controlling propagation. This study introduces an iterative algorithm to estimate infection probability and a mean-field approach for accurate spreading coverage prediction on complex networks.

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Area of Science:

  • Network science
  • Computational epidemiology
  • Data science

Background:

  • Spreading processes on complex networks are widely applicable but predicting their evolution from a single snapshot remains a challenge.
  • Accurate prediction enables proactive control of spreading phenomena, such as disease outbreaks or information diffusion.

Purpose of the Study:

  • To develop a method for predicting the evolution and final coverage of spreading processes on networks.
  • To estimate the infection probability from network propagation data.

Main Methods:

  • An iterative algorithm was developed to estimate the infection probability of a spreading process.
  • A mean-field approach was employed, utilizing the estimated infection probability to predict spreading coverage.
  • The method was validated on both artificial and real-world network structures.

Main Results:

  • The proposed iterative algorithm accurately estimates infection probabilities in spreading processes.
  • The mean-field approach, combined with estimated probabilities, provides reliable predictions of spreading coverage.
  • Validation across diverse network types confirms the method's robustness.

Conclusions:

  • The developed method offers an accurate solution for predicting network spreading evolution and coverage.
  • This work addresses a fundamental gap in understanding and controlling dynamic processes on networks.
  • The findings have implications for managing and mitigating large-scale propagation phenomena.