Modeling Memory Imprints Induced by Interactions in Social Networks

TL;DR

This paper models how social interactions influence memory imprints of relationship importance over time, using cognitive science models and longitudinal data, revealing universal patterns across populations.

Contribution

It adapts a cognitive science model to represent social memory dynamics and demonstrates its predictive power across different populations using call detail data.

Findings

Model accurately predicts relationship strength from social interactions.

Memory imprints show universal patterns across different populations.

Potential for early detection of memory impairments.

Abstract

Memory imprints of the significance of relationships are constantly evolving. They are boosted by social interactions among people involved in relationships, and decay between such events, causing the relationships to change. Despite the importance of the evolution of relationships in social networks, there is little work exploring how interactions over extended periods correlate with people's memory imprints of relationship importance. In this paper, we represent memory dynamics by adapting a well-known cognitive science model. Using two unique longitudinal datasets, we fit the model's parameters to maximize agreement of the memory imprints of relationship strengths of a node predicted from call detail records with the ground-truth list of relationships of this node ordered by their strength. We find that this model, trained on one population, predicts not only on this population but also on a different one, suggesting the universality of memory imprints of social interactions among unrelated individuals. This paper lays the foundation for studying the modeling of social interactions as memory imprints, and its potential use as an unobtrusive tool to early detection of individuals with memory malfunctions.