Prof. Przemyslaw Kazienko presented a talk on "CogSNet: Cognition-driven Social Network" co-authored by Radosław Michalski, Boleslaw K. Szymanski, Christian Lebiere, Omar Lizardo, Marcin Kulisiewicz at NetSci, Paris, June, 2018.

Prof. Przemyslaw Kazienko presented a talk on "CogSNet: Cognition-driven Social Network" co-authored by Radosław Michalski, Boleslaw K. Szymanski, Christian Lebiere, Omar Lizardo, and Marcin Kulisiewicz at NetSci Conference, Paris, June, 2018. The talk focuses on viewing human relations as driven by social events - people interact, exchange information, share knowledge and emotions, or gather news from mass media. These events leave traces in human memory. The initial strength of a trace 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 and explicitly represents human memory dynamics. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves quality of modeling of human interactions in social networks.Human relations are driven by social events - people interact, exchange information, share knowledge and emotions, or gather news from mass media. These events leave traces in human memory. The initial strength of a trace 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 and explicitly represents human memory dynamics. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves quality of modeling of human interactions in social networks. The paper on which the talk is based is available at the link below.