Longitudinal and Dynamic Social Networks

Wasserman/Faust says on p. 731 ""Good, easy-to-use methods for longitudinal network data would be an important addition to the literature."

he also recommends: Wasserman 1978 Iacobucci 1988 Iacobucci 1989,1990 Holland, Leinhardt 1981

More recently <bibtex>@article{snijders2005models,

 title=Template:Models for longitudinal network data,
 author={Snijders, T.A.B.},
 journal={Models and methods in social network analysis},
 pages={215--247},
 year={2005},
 publisher={Cambridge Univ Pr}

}</bibtex>

says " 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 at discrete time points (two or more). Three models are considered in detail, all based on the assump- tion that the observed networks are outcomes of a Markov process evolving in continuous time. The independent arcs model is a trivial baseline model. The reciprocity model expresses effects of reciprocity, but lacks other structural effects. The actor-oriented model is based on a model of actors changing their outgoing ties as a consequence of myopic stochastic optimization of an ob jective function. This frame- work offers the flexibility to represent a variety of network effects. An estimation algorithm is treated, based on a Markov chain Monte Carlo implementation of the method of moments. "

recommending: " Various models have been proposed for the statistical analysis of longitu- dinal social network data. Earlier reviews were given by Wasserman (1978), Frank (1991), and Snijders (1995). "