The main issue for statistical modelling of social networks (represented mathematically mainly by directed graphs) is how to express the dependencies between the ties in the network. This is less complicated for longitudinally than for cross-sectionally observed networks, because the time-ordering assists in the representation of these dependencies. Stochastic actor-oriented models are a class of continuous-time Markov chain models for representing network dynamics. In these models it is assumed that the actors, represented by the nodes in the network, control their outgoing network ties, subject to inertia and contextual constraints, and with an element of randomness to represent the unpredictability of social behaviour. The transition distribution can depend in potentially complex ways on current network structure and monadic or dyadic covariates. Estimation procedures have been developed for such models using network panel data, i.e., repeated measures of the network collected at two or more discrete time points, according to the method of moments, the maximum likelihood principle, as well as Bayesian methods.
The actor-oriented model is presented with an outline of the estimation procedures, and some applications are presented to friendship networks, collaboration networks, and international trade.
Keywords: Social networks; Longitudinal models; Dependence; Markov chains
Biography: Dr Tom A.B. Snijders obtained a doctorate from the University of Groningen and an honorary doctorate from the University of Stockholm. He is Professor of Statistics in the Social Sciences at the University of Oxford, and Professor of Statistics and Methodology at the University of Groningen.