期刊
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
卷 137, 期 12, 页码 3930-3938出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jspi.2007.04.011
关键词
longitudinal social networks; data augmentation; Bayesian inference; random graphs
We consider a continuous-time model for the evolution of social networks. A social network is here conceived as a (di-) graph on a set of vertices, representing actors, and the changes of interest are creation and disappearance over time of (arcs) edges in the graph. Hence we model a collection of random edge indicators that are not, in general, independent. We explicitly model the interdependencies between edge indicators that arise from interaction between social entities. A Markov chain is defined in terms of an embedded chain with holding times and transition probabilities. Data are observed at fixed points in time and hence we are not able to observe the embedded chain directly. Introducing a prior distribution for the parameters we may implement an MCMC algorithm for exploring the posterior distribution of the parameters by simulating the evolution of the embedded process between observations. (c) 2007 Elsevier B.V. All rights reserved.
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