Abstract
Memory imprints of the significance of relationships evolve over time. This evolution is driven by these imprints, gaining strength during interactions between the people involved, and weakening between such events. Despite the importance of understanding this evolution, few research papers explore how long-term interactions in social networks correlate with the 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.
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Acknowledgments
JF and BKS were partially supported by the Army Research Office (ARO) under Grant W911NF-16-1-0524 and by DARPA under Agreements W911NF-17-C-0099 and HR001121C0165. Data collection for the NetHealth project was supported by National Institutes of Health grant #1 R01 HL117757-01A1 (OL Investigator). Data collection for the NetSense project was supported by National Science Foundation grant #0968529 (OL Co-PI).
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Flamino, J., DeVito, R., Lizardo, O., Szymanski, B.K. (2022). Modeling Memory Imprints Induced by Interactions in Social Networks. In: Thomson, R., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2022. Lecture Notes in Computer Science, vol 13558. Springer, Cham. https://doi.org/10.1007/978-3-031-17114-7_18
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DOI: https://doi.org/10.1007/978-3-031-17114-7_18
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