Can we leave the past behind us? Memory decay in temporal social networks
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”. Furthermore, negative past events may have a longer lasting effect on future interactions than positive past events. In this project parametric and semi-parametric methods are developed to study memory decay of past events as a function of their transpired time. The methods are used to study how past interactions affect future interactions from text messages among students.
Arena et al. (2022a). A Bayesian semi-parametric approach for modeling memory decay in dynamic social networks. Sociological Methods & Research.
Arena et al. (2022b). Understanding employee communication with longitudinal social network analysis of email flows. To appear in van de Heuvel, Liebregts, Arjan (Eds.). Data Science for Entrepreneurship.
Arena et al. (2022). How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models.