  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.
