On September 5, Prof. Boleslaw Szymanski presented ICM and Warsaw University seminar entitled "Social Networks through the Prism of Cognition"

On September 5, Prof. Boleslaw Szymanski presented an invited talk entitled "Social Networks through the Prism of Cognition" for ICM and Warsaw University. the talk focuses on differences between how ties in social networks are represented (often as unweighted solid lines) and created (human social interactions are driven by temporary events such as meeting and talking to people, exchanging information, or watching the news). The events leave decaying-in-time traces in human memory. The derived from such traces personal perception of the relevant events, rather than events themselves, drives human decisions and social interactions. In this talk, we discuss how this novel perspective enables us to investigate social tie co-evolution in an attribute-rich social network called NetSense, which was collected on a population of undergraduate students at Notre Dame University for several years. First, we examine the dynamics of co-evolution of two coupled social networks in NetSense. The first is a cognitive network defined by nominations based on perceived prominence collected from repeated surveys of students. The second is built from the behavioral network representing actual interactions between students based on records of their mobile calls and text messages. We address three interrelated questions. First, we ask whether the formation or dissolution of a link in one of the networks precedes or succeeds formation or dissolution of the corresponding link in the other network (temporal dependencies). Second, we explore the causes of observed temporal dependencies between the two networks. For those temporal dependencies that are confirmed, we measure the predictive capacity of such dependencies. Third, we examine whether there are systematic differences in the dissolution rates of symmetric (undirected) versus asymmetric (directed) edges in both networks. Then, we discuss an event-driven temporal complex network model that accounts for the decay mechanism of event memory traces to accurately represent event perception by humans.