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={{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). 
"


Even more recently he gives this overviews of the area of network dynamics.  here is a too big chunk from
<bibtex>@article{snijders2007modeling,
  title={{Modeling the co-evolution of networks and behavior}},
  author={Snijders, T.A.B. and Steglich, C.E.G. and Schweinberger, M.},
  journal={Longitudinal models in the behavioral and related sciences},
  pages={41--71},
  year={2007},
  publisher={Lawrence Erlbaum}
}
</bibtex>

"
On the other side, actors’ characteristics – indicators of performance and 
success, attitudes and other cognitions, behavioral tendencies – can depend 
on the social network the actor is situated in. It is well-known that in many 
social situations, behavior and attitudes of individuals follow patterns of 
assimilation to others to whom they are tied. Examples are the diﬀusion of 
innovations in a professional community (Valente, 1995), pupils’ copying of 
‘chic’ behavior of their friends at school, or traders on a market copying the 
allegedly successful behavior of their competitors. 
The change of network structure is often referred to as selection (Lazars- 
feld and Merton, 1954), and the change of individual characteristics of social 
actors depending on the characteristics of others to whom they are tied is 
called inﬂuence (Friedkin, 1998). It is assumed here that the group of actors 
under study has been delineated in such a way that it is meaningful to inves- 
tigate the selection and inﬂuence processes in this group without considering 
ties to others outside the group. The necessity of studying selection and in- 
ﬂuence processes in networks simultaneously was discussed both in detailed 
network investigations (e.g., Padgett and Ansell, 1993) and in theoretical dis- 
cussion essays (Emirbayer and Goodwin, 1994, Doreian and Stokman, 1997). 
A concrete example is smoking initiation among adolescents, where it has 
been established in the literature that friends tend to have similar patterns 
of smoking behavior, but where it is unknown to which extent this is a mat- 
ter of selection of friends on the basis of common behavior, or adaptation of 
2
behavior towards that of one’s friends (Bauman and Ennett, 1996). 
This chapter proposes a statistical method for investigating network struc- 
ture together with relevant actor attributes as joint dependent variables in a 
longitudinal framework, assuming that data have been collected according to 
a panel design. This is a more detailed exposition of the proposals sketched 
in Steglich et al. (2004). In the stochastic model, the network structure and 
the individual attributes evolve simultaneously in a dynamic process. The 
method is illustrated by an example on the dynamics of alcohol consumption 
among adolescent friends. 
...
The principles of actor-driven, or actor-oriented, modeling were proposed in Snijders (1996). The model for dynamics of only networks, without behav- 
ior, was formulated in Snijders (2001, 2005). In Steglich et al. (2004), the sociological aspects of the model for dynamics of networks and behavior are discussed, with an extensive example about the interrelationship of the de- velopment of friendship networks and the dynamics in smoking and drinking behavior, on the basis of data from a Scottish high school. This chapter gives an overview of the speciﬁcation of the stochastic model for dynamics of networks and behavior and then proceeds to parameter estimation and model selection. 
"