This webpage contains statistical and methodological papers about SIENA.

Manual

• Snijders, Tom A.B., Steglich, Christian E.G., Michael Schweinberger and Mark Huisman. Manual for SIENA version 3.1. University of Groningen: ICS / Department of Sociology; University of Oxford: Department of Statistics, (2007).
• However, it is better to use the provisional Siena 3.2 manual;
• or for RSiena users, the provisional Siena 4 manual.

Longitudinal network data

• The transparencies of the workshop The analysis of longitudinal social network data held at Sunbelt Social Networks Conferences.
• Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2009). Introduction to actor-based models for network dynamics (preprint). Social Networks, in press.
  This is a tutorial.
  http://dx.doi.org/10.1016/j.socnet.2009.02.004.
• Snijders, Tom A.B. (2005). Models for Longitudinal Network Data.
  Chapter 11 in P. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis. New York: Cambridge University Press.
• Snijders, T.A.B., 2006. Statistical Methods for Network Dynamics. In: S.R. Luchini et al. (eds.), Proceedings of the XLIII Scientific Meeting, Italian Statistical Society, pp. 281--296. Padova: CLEUP.
  (This is an introduction for statistically oriented researchers.)

Longitudinal data of networks and behavior

• Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2009). Introduction to actor-based models for network dynamics (preprint). Social Networks, in press.
  This is a tutorial.
  http://dx.doi.org/10.1016/j.socnet.2009.02.004.
• Steglich, C.E.G., Snijders, T.A.B. and Pearson, M. Dynamic Networks and Behavior: Separating Selection from Influence.
  To be published, Sociological Methodology, 2010.
• Snijders, Tom A.B. (2009). Longitudinal Methods of Network Analysis .
  Pp. 5998-6013 in Encyclopedia of Complexity and System Science (editor-in-chief Bob Meyers), part of the Social Networks section (section editor John Scott), Springer Verlag, 2009.

Exponential random graph models

• An introduction to Exponential Random Graph Models is given at the MelNet site with a list of preprints of the Melbourne social networks group.
• Garry L. Robins, Pip Pattison, Yuval Kalish, and Dean Lusher, An introduction to exponential random graph (p*) models for social networks. Social Networks 29, 173-191 (2007).
  
• Robins, G.L., Snijders, T.A.B., Wang, P., Handcock, M., & Pattison, P. Recent developments in exponential random graph (p*) models for social networks. Social Networks 29, 192-215 (2007).
  

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

• de Federico de la Rua, Ainhoa (2004), L'Analyse Longitudinale de Reseaux sociaux totaux avec SIENA - Methode, discussion et application. BMS, Bulletin de Methodologie Sociologique, 84, October 2004, 5-39.

In German

• Steglich, Christian, and Knecht, Andrea (2009), Die statistische Analyse dynamischer Netzwerkdaten. In: Christian Stegbauer and Roger Häußling (Eds.), Handbuch der Netzwerkforschung, Wiesbaden (Verlag für Sozialwissenschaften).

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.

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.

Longitudinal network data

• Schweinberger, M., (2005). Statistical modeling of network panel data: Goodness of fit.
  
• Snijders, T.A.B., Stochastic actor-oriented dynamic network analysis.
  Journal of Mathematical Sociology, 21 (1996), 149-172.
  This paper is a precursor: it is about a method not implemented in SIENA, but along the same lines, for data in the form of ranks.
• Snijders, Tom A.B., The statistical evaluation of social network dynamics.
  Pp. 361-395 in Sociological Methodology - 2001, edited by M.E. Sobel and M.P. Becker. Boston and London: Basil Blackwell.
• Snijders, Tom A.B. (2008). Statistical modeling of dynamics of non-directed networks.
  Presentation at the XXV International Sunbelt Social Networks Conference, Redondo Beach (Los Angeles), February 16-20. 2005. Revised version.
• Snijders, Tom A.B. and Van Duijn, Marijtje A.J. (1997). Simulation for statistical inference in dynamic network models.
  In: Conte, R., Hegselmann, R. Terna, P. (eds.), Simulating social phenomena , 493-512. Berlin: Springer.
  

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.

Exponential random graph models

• Snijders, Tom A.B. (2002). Markov chain Monte Carlo estimation of exponential random graph models.
  Journal of Social Structure, Vol. 3, No. 2.
  (The JoSS version contains a Java program which is not contained in the pdf file.)
• Snijders, Tom A.B., Pattison, Philippa E., Robins, Garry L., and Handcock, Mark S., New specifications for exponential random graph models.
  Sociological Methodology, 2006, 99-153.

Longitudinal network data

• Huisman, Mark, and Snijders, Tom A.B., (2003). Statistical analysis of longitudinal network data with changing composition.
  Sociological Methods & Research, 32, 253-287.
• Huisman, Mark, and Steglich, C.E.G., (2008). Treatment of non-response in longitudinal network data..
  Social Networks, 30, 297-308.
• Koskinen, Johan H., and Snijders, Tom A.B., (2007). Bayesian inference for dynamic social network data. Journal of Statistical Planning and Inference, 137 (2007), 3930-3938.
  
• Schweinberger, M., and Snijders, T.A.B., (2007). Markov models for digraph panel data: Monte Carlo-based derivative estimation.
  Computational Statistics and Data Analysis 51, 4465-4483.
• Snijders, Tom A.B (2003). Accounting for Degree Distributions in Empirical Analysis of Network Dynamics.
  Pp. 146-161 in: R. Breiger, K. Carley, and P. Pattison (eds.), Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers.
  National Research Council of the National Academies. Washington, DC: The National Academies Press.
  This is about an alternative model for network evolution which gives more flexibility in modeling the distribution of the out-degrees.
• Snijders, Tom A.B. (2004). Explained Variation in Dynamic Network Models.
  Mathematiques, Informatique et Sciences Humaines / Mathematics and Social Sciences, 168, 2004(4), p. 31-41.

Longitudinal data of networks and behavior

• --

Exponential random graph models

• Snijders, Tom A.B., and van Duijn, Marijtje A.J. (2002). Conditional Maximum Likelihood Estimation under Various Specifications of Exponential Random Graph Models.
  Pp. 117-134 in contributions to Social Network Analysis, Information Theory, and Other Topics in Statistics; A Festschrift in honour of Ove Frank (ed. Jan Hagberg).

Multilevel dynamic network analysis

• Snijders, Tom A.B, and Baerveldt, Chris, A Multilevel Network Study of the Effects of Delinquent Behavior on Friendship Evolution.
  Journal of Mathematical Sociology 27 (2003), 123-151.
  This is about a 'multilevel' or 'meta-analysis' extension of this longitudinal method to the case of observations on several networks, implemented in Siena08. Further work on such extensions is under way.
  A set of transparencies explaining this method can be downloaded here.

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