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Articles about the SIENA program

This webpage contains statistical and methodological papers about SIENA, as well as a lot of teaching materials.


  • Ruth M. Ripley, Tom A.B. Snijders, Zsófia Boda, Andras Vörös, and Paulina Preciado (2020). Manual for SIENA version 4.0. Oxford: University of Oxford, Department of Statistics; Nuffield College.
  • Tom A.B. Snijders (2019). 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

Also see the section 'Tutorial / review articles' on the Siena applications page.

Longitudinal network data

Video presentations

Longitudinal data of networks and behavior

More general reviews including actor-oriented models

In Chinese
  • Tom A.B. Snijders (2018) [2011], Network Dynamics . Chinese translation (2018) for Chongqing University Press of Network Dynamics, Chapter 33 (pp. 501-513) in John Scott and Peter J. Carrington (eds.), The SAGE Handbook of Social Network Analysis. London: Sage, 2011.

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
  • Savoia, Laura (2007), L'analisi della dinamica del network con SIENA. In: A. Salvini (a cura di), Analisi delle reti sociali. Teorie, metodi, applicazioni, Milano: FrancoAngeli.

    (The software demonstration here is outdated; it uses the old StOCNET implementation.)

In Spanish and Catalan

Presentations (teaching material)


Specific topics

Literature with fundamental statistical description of the methods

Longitudinal network data

Longitudinal data of networks and behavior

Further literature about special topics

Longitudinal network data

Design of longitudinal network studies, power issues

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
  • Tom A.B. Snijders, Alessandro Lomi, and Vanina Jasmine Torló (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35, 265-276.
    In Figure 2, Y_{ia} and Y_{ja} should be interchanged.

    This paper presents stochastic actor-oriented models for one-mode / two-mode network coevolution.

  • Kayo Fujimoto, Tom A.B. Snijders, and Thomas W. Valente (2018). Multivariate dynamics of one-mode and two-mode networks: Explaining similarity in sports participation among friends. Network Science, 6, 370-395.

    This paper furter develops modeling of one-mode / two-mode network coevolution, applied to the dynamic interplay between the two-mode affiliation network of adolescents' participation in sports activities and the one-mode friendship network. Methodologically it is about the two mechanisms for mixed transitive closure: affiliation-based focal closure, where individuals participating in the same sport become (or stay) friends; and association-based affiliation closure, where individuals start (or continue) participating in the sports in which their friends are already active. As a corresponding descriptive method generalizing the 'pie charts' of Steglich et al. (2010), we propose a quantitative measure for the relative strength of these two mechanisms as explanations of the association between the one-mode and the two-mode network. A further methodological issue is that two specifications are given to represent homophily effects in the two-mode network.

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.
  • Robert W. Krause, Mark Huisman, and Tom A.B. Snijders (2018). Multiple imputation for longitudinal network data. Italian Journal of Applied Statistics, 30, 33-57.

    Missing data on network ties are a fundamental problem for network analysis. In this paper, we present a new method with two variants to handle missing data due to actor non-response in the framework of stochastic actor-oriented models (SAOMs). The proposed method imputes missing tie variables in the first wave either by using a Bayesian exponential random graph model (BERGMs) or a stationary SAOM and imputes missing tie variables in later waves utilizing a SAOM. The proposed method is compared to the standard SAOM missing data treatment as well as recently proposed methods. The multiple imputation procedure provided more reliable point estimates than the default treatment.

    A script for this paper, and another one for related missing data issues, is at the Siena scripts page.

  • 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.

  • Tjeerd Zandberg and Mark Huisman (2019). Missing behavior data in longitudinal network studies: The impact of treatment methods on estimated effect parameters in stochastic actor oriented models. Social Network Analysis and Mining, 9.8.

    This paper examines seven different methods that are currently available to deal with missing behavior data. The effect of the missing data methods was inspected using three criteria: model convergence, parameter bias, and parameter coverage. The results show that, in general, the default method available in the RSIENA software gives the best outcomes for all three criteria.

  • 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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