Statistical Analysis of Longitudinal Network Data With Changing Composition

Markov chains can be used for the modeling of complex longitudinal network data. One class of probability models to model the evolution of social networks are stochastic actor-oriented models for network change proposed by Snijders. These models are continuous-time Markov chain models that are imple...

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Bibliographic Details
Published in:Sociological methods & research 2003-11, Vol.32 (2), p.253-287
Main Authors: Huisman, Mark, Snijders, Tom A. B.
Format: Article
Language:English
Subjects:
Data analysis
History, theory and methodology
Markov Process
Markov Processes
Markovian processes
Methodology
Network analysis
Networks
Research methods
Simulation
Sociology
Statistical analysis
Statistics
Stochastic Models
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Summary:Markov chains can be used for the modeling of complex longitudinal network data. One class of probability models to model the evolution of social networks are stochastic actor-oriented models for network change proposed by Snijders. These models are continuous-time Markov chain models that are implemented as simulation models. The authors propose an extension of the simulation algorithm of stochastic actor-oriented models to include networks of changing composition. In empirical research, the composition of networks may change due to actors joining or leaving the network at some point in time. The composition changes are modeled as exogenous events that occur at given time points and are implemented in the simulation algorithm. The estimation of the network effects, as well as the effects of actor and dyadic attributes that influence the evolution of the network, is based on the simulation of Markov chains.
ISSN:0049-1241
1552-8294
DOI:10.1177/0049124103256096