A Bayesian semi-parametric approach for modeling memory decay in dynamic social networks

Guiseppe Arena*, Joris Mulder, Roger Leenders

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
82 Downloads (Pure)

Abstract

In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors’ decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that occurred a long time ago–a phenomenon known as “memory decay”. Previous studies either predefined a short-run and long-run memory or fixed a parametric exponential memory decay using a predefined half-life period. In real-life relational event networks, however, it is generally unknown how the influence of past events fades as time goes by. For this reason, it is not recommendable to fix memory decay in an ad-hoc manner, but instead we should learn the shape of memory decay from the observed data. In this paper, a novel semi-parametric approach based on Bayesian Model Averaging is proposed for learning the shape of the memory decay without requiring any parametric assumptions. The method is applied to relational event history data among socio-political actors in India and a comparison with other relational event models based on predefined memory decays is provided.
Original languageEnglish
Number of pages51
JournalSociological Methods & Research
DOIs
Publication statusE-pub ahead of print - 2023

Keywords

  • Relational event model
  • Social network analysis
  • event-history data
  • Bayesian Model Averaging
  • Network dynamics
  • memory decay
  • memory retention process

Fingerprint

Dive into the research topics of 'A Bayesian semi-parametric approach for modeling memory decay in dynamic social networks'. Together they form a unique fingerprint.

Cite this