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Statistical Analysis of Longitudinal Network Data With Changing Composition

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  • Mark Huisman
  • Tom A. B. Snijders

Abstract

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.

Suggested Citation

  • Mark Huisman & Tom A. B. Snijders, 2003. "Statistical Analysis of Longitudinal Network Data With Changing Composition," Sociological Methods & Research, , vol. 32(2), pages 253-287, November.
    • Handle: RePEc:sae:somere:v:32:y:2003:i:2:p:253-287
    DOI: 10.1177/0049124103256096
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    References listed on IDEAS

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    1. Gerhard G. Van De Bunt & Marijtje A.J. Van Duijn & Tom A.B. Snijders, 1999. "Friendship Networks Through Time: An Actor-Oriented Dynamic Statistical Network Model," Computational and Mathematical Organization Theory, Springer, vol. 5(2), pages 167-192, July.
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    Cited by:

    1. Lopaciuk-Gonczaryk, Beata, 2019. "Social Capital Formation Through a Polish LETS: Challenging the Presumed Merits of Local Currencies," Ecological Economics, Elsevier, vol. 158(C), pages 75-87.
    2. Mary F. McGuire, 2014. "Pancreatic Cancer: Insights from Counterterrorism Theories," Decision Analysis, INFORMS, vol. 11(4), pages 265-276, December.
    3. Elynn Y. Chen & Rong Chen, 2019. "Modeling Dynamic Transport Network with Matrix Factor Models: with an Application to International Trade Flow," Papers 1901.00769, arXiv.org.
    4. Johnson, Jeffrey C. & Luczkovich, Joseph J. & Borgatti, Stephen P. & Snijders, Tom A.B., 2009. "Using social network analysis tools in ecology: Markov process transition models applied to the seasonal trophic network dynamics of the Chesapeake Bay," Ecological Modelling, Elsevier, vol. 220(22), pages 3133-3140.
    5. de la Haye, Kayla & Robins, Garry & Mohr, Philip & Wilson, Carlene, 2011. "How physical activity shapes, and is shaped by, adolescent friendships," Social Science & Medicine, Elsevier, vol. 73(5), pages 719-728, September.
    6. Mercken, Liesbeth & Snijders, Tom A.B. & Steglich, Christian & de Vries, Hein, 2009. "Dynamics of adolescent friendship networks and smoking behavior: Social network analyses in six European countries," Social Science & Medicine, Elsevier, vol. 69(10), pages 1506-1514, November.
    7. Antonio Zinilli, 2016. "Competitive project funding and dynamic complex networks: evidence from Projects of National Interest (PRIN)," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 633-652, August.

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