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Title: Modeling the dynamics of social network perceptions Authors:  Nynke Niezink - Carnegie Mellon University (United States) [presenting]
Abstract: To understand and predict the behavioral consequences of social networks, it is important to understand how social networks form. Studies of network dynamics usually rely on data of the network ties (e.g., friendship or collaboration) among a group of social actors, such as people or organizations, collected at multiple measurement moments. Many studies have shown that individuals differ in how they perceive and cognitively represent the networks they are embedded in. However, in the analysis of network dynamics, this is not taken into account. Instead, current models assume individuals to make network decisions, creating and dissolving ties, based on a shared network representation. We propose a model for the dynamics of social networks taking individuals network perceptions into account. This model generalizes the stochastic actor-oriented model, a continuous-time Markov chain model on the state space of all possible networks among a group of actors, to simultaneously model the network as perceived by all actors.