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

TL;DR

This paper introduces a Bayesian semi-parametric method to learn how memory decay influences social interactions over time in dynamic networks, avoiding fixed assumptions about decay shape.

Contribution

It proposes a novel Bayesian Model Averaging approach to flexibly model memory decay without parametric constraints, applied to real-world relational event data.

Findings

Effectively captures varying memory decay shapes from data

Demonstrates improved modeling of social interactions over fixed parametric methods

Provides insights into the temporal influence of past events in social networks

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 quantity of past interactions and the time that elapsed since the past interactions occurred affect the actors' decision-making to interact with other actors in the network. Recently occurred events generally 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 using a predefined half-life period. In real-life relational event networks however it is generally unknown how the memory of actors about the past events fades as time goes by. For this reason it is not recommendable to fix this 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.