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Annual Review of Statistics and Its Application

Volume 4, 2017

Stochastic Actor-Oriented Models for Network Dynamics

Abstract

This article discusses the stochastic actor-oriented model for analyzing panel data of networks. The model is defined as a continuous-time Markov chain, observed at two or more discrete time moments. It can be regarded as a generalized linear model with a large amount of missing data. Several estimation methods are discussed. After presenting the model for evolution of networks, attention is given to coevolution models. These use the same approach of a continuous-time Markov chain observed at a small number of time points, but now with an extended state space. The state space can be, for example, the combination of a network and nodal variables, or a combination of several networks. This leads to models for the dynamics of multivariate networks. The article emphasizes the approach to modeling and algorithmic issues for estimation; some attention is given to comparison with other models.

Keyword(s): inference, social networks, statistical modeling
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/content/journals/10.1146/annurev-statistics-060116-054035
2017-03-07
2024-06-17
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/content/journals/10.1146/annurev-statistics-060116-054035
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Stochastic Actor-Oriented Models for Network Dynamics
Annual Review of Statistics and Its Application 4, 343 (2017); https://doi.org/10.1146/annurev-statistics-060116-054035
/content/journals/10.1146/annurev-statistics-060116-054035
/content/journals/10.1146/annurev-statistics-060116-054035
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