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Articles about the SIENA program

This webpage contains statistical and methodological papers about SIENA.


  • Ruth M. Ripley, Tom A.B. Snijders, Zsófia Boda, Andras Vörös, and Paulina Preciado (2017). Manual for SIENA version 4.0. Oxford: University of Oxford, Department of Statistics; Nuffield College.
  • Tom A.B. Snijders (2017). Siena algorithms. This paper gives a sketch of the main algorithms used in RSiena. It is meant as background material for understanding the code of RSiena.
  • For remaining users of Siena 3:
    Tom A.B. Snijders, Christian E.G. Steglich, Michael Schweinberger, and Mark Huisman (2010). Manual for SIENA version 3.2. University of Groningen: ICS / Department of Sociology; University of Oxford: Department of Statistics, (2010).
  • (Note that Siena 3 is no longer supported, and any remaining users are being advised to switch to RSiena! This program is still mentioned here for those who wish to use its functionality for estimation of Exponential Random Graph Models).

Introductory literature

Longitudinal network data

Longitudinal data of networks and behavior

More general reviews including actor-oriented models

In Dutch
  • Huisman, Mark, and Snijders, Tom A.B. (2003), Een stochastisch model voor netwerkevolutie. Nederlands Tijdschrift voor de Psychologie, 58, 182-194.

In French

In German

In Italian

In Spanish and Catalan
  • Jariego, Isidro Maya, and de Federico de la Rua, Ainhoa (2006), El analisis dinamico de redes sociales con SIENA. In: Jose Luis Molina, Agueda Quiroga, Joel Marti, Isidro Maya Jariego, and Ainhoa de Federico (eds.), Talleres de autoformacion con progamas informaticos de analisis de redes sociales, Bellaterra: Universitat Autonoma de Barcelona, Servei de Publicacions.
  • de Federico de la Rua, Ainhoa (2005), El analisis dinamico de redes sociales con SIENA. Metodo, Discusion y Aplicacion. Empiria, 10, 151-181. A preprint is available here.

Presentations (teaching material)

Literature with fundamental statistical description of the methods

Longitudinal network data

Longitudinal data of networks and behavior
  • Snijders, Tom A.B., Steglich, Christian E.G., and Schweinberger, Michael, Modeling the co-evolution of networks and behavior.
    Pp. 41-71 in Longitudinal models in the behavioral and related sciences, edited by Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum, 2007.

    (Develops the estimation for dynamics of networks and behavior by the Method of Moments.) (Formula (29) in this chapter is incorrect, the condition should be just the other way around. The text is correct.)

Further literature about special topics

Longitudinal network data

Longitudinal data of networks and behavior

Dynamic Exponential Random Graph Models
  • Tom Snijders and Johan Koskinen, Longitudinal Models. Chapter 11 (pp. 130-140) in Exponential Random Graph Models for Social Networks, D. Lusher, J. Koskinen, and G. Robins (eds.), Cambridge University Press, 2013.
Co-evolution of multiple networks

Missing data
  • Kayla de la Haye, Joshua Embree, Marc Punkay, Dorothy L. Espelage, Joan S. Tucker, and Harold D. Green Jr. (2017). Analytic strategies for longitudinal networks with missing data. Social Networks, 50, 17-25.
    Proposes a strategy for dealing with varying actors subsets having missing data. The strategy is based on the multiple groups option.
  • Hipp, J.R., Wang, C., Butts, C.T., Jose, R., Lakon, C.M. (2015). Research note: the consequences of different methods for handling missing network data in stochastic actor based models. Social Networks 41, 56-71.

    This paper presents a method for handling missing data in longitudinal network modeling that is better than the simple method employed in RSiena. However, the method in RSiena is not as poor as these authors suggest. The paper misrepresents details of the way in which RSiena handles missing data. The writing suggests that the 'third strategy' mentioned on p. 57 is the strategy employed by RSiena. This is not the case, because although RSiena uses the imputation strategy as described here, it also excludes the tie and behavior variables for which a data values is missing at a wave and/or a preceding wave from the calculation of the target statistics for this wave. Thus, the effect of the imputation is minimized. See Huisman and Steglich (2008, Section 4.4), and the RSiena Manual, Section 4.3.2.

  • Huisman, Mark, and Steglich, C.E.G., (2008). Treatment of non-response in longitudinal network data.
    Social Networks, 30, 297-308.
  • Cheng Wang, Carter T. Butts, John R. Hipp, Rupa Jose, and Cynthia M. Lakon (2016). Multiple imputation for missing edge data: A predictive evaluation method with application to Add Health. Social Networks 45, 89-98.

    See the note about the paper by Hipp et al. (2015), a few lines up. This paper likewise misrepresents the way in which RSiena handles missing data. The phrase 'it simply drops them or treats them as absent' (p. 95) is incorrect: the imputation used in RSiena by 'last observation carried forward' is somewhat more informative than this, and the exclusion of the imputed data from the target statistics means that 'simply' is not applicable. Further, 'As Hipp et al. (2015) found' is incorrect, because Hipp et al. (2015) used a different imputation strategy in their comparisons, as mentioned above.

  • Ulrik Brandes, Natalie Indlekofer, and Martin Mader (2012). Visualization methods for longitudinal social networks and stochastic actor-oriented modeling. Social Networks, 34, 291-308. DOI:

Multilevel dynamic network analysis and meta-analysis of network dynamics

Literature about further applications can be found at the webpage with applications.

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