Visualization Methods for 1 Longitudinal Social Networks and 2 Stochastic Actor - Oriented Modeling ?
نویسندگان
چکیده
6 As a consequence of the rising interest in longitudinal social networks and their 7 analysis, there is also an increasing demand for tools to visualize them. We argue 8 that similar adaptations of state-of-the-art graph-drawing methods can be used to 9 visualize both, longitudinal networks and predictions of stochastic actor-oriented 10 models (SAOM), the most prominent approach for analyzing such networks. The 11 proposed methods are illustrated on a longitudinal network of acquaintanceship 12 among university freshmen. 13
منابع مشابه
Visualization methods for longitudinal social networks and stochastic actor-oriented modeling
As a consequence of the rising interest in longitudinal social networks and their analysis, there is also an increasing demand for tools to visualize them. We argue that similar adaptations of state-of-the-art graph-drawing methods can be used to visualize both, longitudinal networks and predictions of stochastic actor-oriented models (SAOM), the most prominent approach for analyzing such netwo...
متن کاملVisualization Methods for Longitudinal Social 1 Networks and Actor - based Modeling ?
5 As a consequence of the rising interest in longitudinal social networks and their 6 analysis, there is also an increasing demand for tools to visualize them. We argue 7 that similar adaptations of state-of-the-art graph-drawing methods can be used to 8 visualize longitudinal networks and the fit of actor-based models, the most promi9 nent approach for analyzing such networks. The proposed met...
متن کاملStatistical Analysis of Longitudinal Network Data with Changing Composition
Markov chains can be used for the modeling of complex longitudinal network data. One class of probability models to model the evolution of social networks are stochastic actor-oriented models for network change proposed by Snijders (1996, 2001). These models are continuous-time Markov chain models that are implemented as simulation models. In this paper an extension of the simulation algorithm ...
متن کاملModels for Longitudinal Network Data
This chapter treats statistical methods for network evolution. It is argued that it is most fruitful to consider models where network evolution is represented as the result of many (usually non-observed) small changes occurring between the consecutively observed networks. Accordingly, the focus is on models where a continuous-time network evolution is assumed although the observations are made ...
متن کاملRelative importance of effects in stochastic actor-oriented models
A measure of relative importance of network effects in the stochastic actor-oriented model (SAOM) is proposed. The SAOM is a parametric model for statistical inference in longitudinal social networks. The complexity of the model makes the interpretation of inferred results difficult. So far, the focus is on significance tests while the relative importance of effects is usually ignored. Indeed, ...
متن کامل