Here - Tilburg University

KEY NOTE SPEAKER
Snijders, Tom A.B. Tom.Snijders@nuffield.ox.ac.uk
Nuffield College
University of Oxford
Title
Statistical models for dynamics of social networks: inference and applications
Abstract
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. These models assume 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 a review is given of some of the applications that have
appeared in the literature.