Year: 2021
Type: article
Abstract: Human relations are driven by social events—people interact, exchange information, share knowledge and emotions, and gather news from mass media. These events leave traces in human memory, the strength of which depends on cognitive factors such as emotions or attention span. Each trace continuously weakens over time unless another related event activity strengthens it. Here, we introduce a novel cognition‐driven social network (CogSNet) model that accounts for cognitive aspects of social perception. The model explicitly represents each social interaction as a trace in human memory with its corresponding dynamics. The strength of the trace is the only measure of the influence that the interactions had on a person. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves the quality of modeling of human interactions in social networks.
Source: Complexity
DOI: 10.1155/2021/4963903
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,760
Funders: National Science Foundation, Sprint, University of Notre Dame, European Commission, Narodowe Centrum Nauki, Narodowym Centrum Nauki, Ministerstwo Edukacji i Nauki, Horizon 2020 Framework Programme, Wroclawskie Centrum Sieciowo-Superkomputerowe, Politechnika Wroclawska, Office of Naval Research, Army Research Office
Awards: G1185067389, Reverse EngiNeering of sOcial Information pRocessing, G1522879398, G2988320508, SoCS: Explorations on the Effects of Pervasive Networking on Social Relationships and Resource Planning, G4278024187, G4752947285, G4937468798, G540615712, G657448715, G684190735, G7452299184, G8876996369
OpenAlex ID: W2807762952