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

by   Giuseppe Arena, et al.

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.


page 1

page 2

page 3

page 4


Bayesian mixed-effect models for independent dynamic social network data

Relational event or time-stamped social network data have become increas...

All that Glitters is not Gold: Modeling Relational Events with Measurement Errors

As relational event models are an increasingly popular model for studyin...

Separating the Wheat from the Chaff: Bayesian Regularization in Dynamic Social Networks

In recent years there has been an increasing interest in the use of rela...

Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability

A key capability of an intelligent system is deciding when events from p...

Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes

Temporal Point Processes (TPP) are probabilistic generative frameworks. ...

Modeling Memory Imprints Induced by Interactions in Social Networks

Memory imprints of the significance of relationships are constantly evol...

Relational Event Modeling

Advances in information technology have increased the availability of ti...

Please sign up or login with your details

Forgot password? Click here to reset