  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.
