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.

翻译：关系重要性的记忆印记持续演化，它们因关系双方的社会互动而增强，并在事件间隔期间衰减，从而引发关系变化。尽管社交网络中关系演化具有重要意义，但关于长时间跨度的互动如何影响人们对关系重要性的记忆印记的研究仍较为有限。本文通过改编认知科学领域的经典模型对记忆动态进行表征。利用两组独特的纵向数据集，我们拟合模型参数，使得基于通话详细记录预测的节点关系强度记忆印记，与该节点按强度排序的关系真实列表达成最大一致性。研究发现，该模型在某一群体上训练后，不仅能预测该群体，还能预测不同群体的数据，这暗示了非亲缘个体间社会互动记忆印记的普适性。本文为将社会互动建模为记忆印记的研究奠定基础，并探索其作为早期检测记忆功能障碍个体非侵入性工具的潜力。