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Investigating network structures in recurrent event data with discrete observation times.

Yufeng Xia1, Yangkuo Li1, Xiaobing Zhao2

  • 1School of Data Sciences, Zhejiang University of Finance and Economics, Xueyuan Street, Hangzhou, 310018, Zhejiang Province, China.

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Area of Science:

  • Network Science
  • Statistical Modeling
  • Epidemiology

Background:

  • Longitudinal networks are crucial for understanding dynamic systems.
  • Recurrent event processes are common in real-world interactions.
  • Discrete observation times present unique analytical challenges.

Purpose of the Study:

  • To develop a statistical framework for analyzing pairwise interactions in longitudinal networks with recurrent events.
  • To adapt the stochastic block model for discrete time observations.
  • To estimate interaction intensity functions accurately.

Main Methods:

  • Utilized the stochastic block model framework.
  • Applied variational EM algorithm and variational maximum likelihood estimation.
  • Employed a novel method for estimating edge intensity functions using a distribution function F and self-consistency algorithm.

Main Results:

  • The proposed estimation procedure effectively uncovers underlying structures in longitudinal networks.
  • Numerical simulations demonstrate the method's performance.
  • The model successfully analyzes real-world interaction data.

Conclusions:

  • The developed statistical approach provides robust inference for recurrent event processes in longitudinal networks.
  • This method enhances the analysis of dynamic interaction data, particularly with discrete observations.
  • The findings have implications for network analysis and disease monitoring.