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Dynamic relational event modeling: Testing, exploring, and applying.

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This study introduces a dynamic relational event model (REM) to analyze how social network effects change over time. Bayesian methods help detect these changes and optimize analysis windows for accurate network evolution insights.

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Area of Science:

  • Social Network Analysis
  • Statistical Modeling
  • Network Science

Background:

  • Relational event models (REM) analyze time-ordered social interactions but assume static network effects.
  • Real-world social networks often exhibit dynamic, time-varying network effects, limiting basic REM applicability.
  • Understanding evolving network drivers is crucial for accurate social interaction sequence analysis.

Purpose of the Study:

  • To extend the relational event model (REM) framework for testing and exploring time-varying network effects.
  • To develop Bayesian approaches for detecting changes in network effects and optimizing analysis parameters.
  • To provide methods for analyzing dynamic network evolution in relational event history data.

Main Methods:

  • Developed a Bayesian test to determine if network effects change over a study period, comparing static and dynamic REM.
  • Utilized a moving window approach within a dynamic REM to study time-varying effects, assessing window width impacts.
  • Created a Bayesian method for empirically determining optimal window widths for dynamic REM analysis.

Main Results:

  • The Bayesian test accurately quantifies evidence for static versus dynamic REM.
  • Simulation studies show window width impacts accuracy and precision in dynamic REM.
  • Empirically determined window widths balance accuracy during effect changes and precision during stable periods.

Conclusions:

  • The proposed Bayesian methods effectively test for and explore time-varying network effects in relational event history data.
  • Dynamic REM with optimized window widths provides a powerful tool for understanding evolving social network structures.
  • These approaches offer valuable insights into real-world network dynamics, such as workplace interactions.