Introduction to stochastic actor-based models for network dynamics |
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Authors: | Tom AB Snijders Gerhard G van de Bunt Christian EG Steglich |
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Institution: | 1. University of Oxford and University of Groningen, Grote Rozenstraat 31, 9712 TG Groningen, Netherlands;2. Free University, Amsterdam, Netherlands;3. University of Groningen, Netherlands |
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Abstract: | Stochastic actor-based models are models for network dynamics that can represent a wide variety of influences on network change, and allow to estimate parameters expressing such influences, and test corresponding hypotheses. The nodes in the network represent social actors, and the collection of ties represents a social relation. The assumptions posit that the network evolves as a stochastic process ‘driven by the actors’, i.e., the model lends itself especially for representing theories about how actors change their outgoing ties. The probabilities of tie changes are in part endogenously determined, i.e., as a function of the current network structure itself, and in part exogenously, as a function of characteristics of the nodes (‘actor covariates’) and of characteristics of pairs of nodes (‘dyadic covariates’). In an extended form, stochastic actor-based models can be used to analyze longitudinal data on social networks jointly with changing attributes of the actors: dynamics of networks and behavior. |
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Keywords: | Statistical modeling Longitudinal Markov chain Agent-based model Peer selection Peer influence |
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