Articles about the SIENA program

This webpage contains statistical and methodological papers about SIENA.

Manual

Ripley, Ruth M., Snijders, Tom A.B., and Preciado, Paulina. 2011. Manual for SIENA version 4.0. Oxford: University of Oxford, Department of Statistics; Nuffield College.
For remaining users of Siena 3:
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);
or the provisional Siena 3.2 manual.

Introductory literature

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., van de Bunt, G.G., and Steglich, C.E.G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44-60.
This is a tutorial.
DOI: 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, pp. 215-247.
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., van de Bunt, G.G., and Steglich, C.E.G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44-60.
This is a tutorial.
DOI: http://dx.doi.org/10.1016/j.socnet.2009.02.004.
Steglich, C.E.G., Snijders, T.A.B. and Pearson, M. (2010). Dynamic Networks and Behavior: Separating Selection from Influence.
Sociological Methodology, 40, 329-393.
DOI: http://dx.doi.org/10.1111/j.1467-9531.2010.01225.x .
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.

Literature with fundamental statistical description of the methods

Longitudinal network data

Koskinen, Johan, and Edling, Christopher (2010). Modelling the evolution of a bipartite network - Peer referral in interlocking directorates.
Social Networks, in press.
DOI: http://dx.doi.org/10.1016/j.socnet.2010.03.001.
Schweinberger, M., (2011). Statistical modeling of network panel data: Goodness of fit.
British Journal of Statistical and Mathematical Psychology, in press.
DOI: http://dx.doi.org/10.1111/j.2044-8317.2011.02022.x.
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. DOI: http://dx.doi.org/10.1111/0081-1750.00099.
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., Koskinen, Johan, and Schweinberger, Michael (2010). Maximum Likelihood Estimation for Social Network Dynamics. Annals of Applied Statistics 4, 567-588.
DOI: http://dx.doi.org/10.1214/09-AOAS313. arXiv: http://arxiv.org/abs/1011.1753.
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.

Further literature about special topics

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.
Joshua A. Lospinoso, Michael Schweinberger, Tom A. B. Snijders and Ruth M. Ripley (2011). Assessing and accounting for time heterogeneity in stochastic actor oriented models..
Advances in Data Analysis and Classification, 5, 147-176.
DOI: http://dx.doi.org/10.1007/s11634-010-0076-1.
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.
Snijders, Tom A.B. (2011). Statistical models for dynamics of social networks: inference and applications. Proceedings, 50th Congress of the International Statistical Institute, Dublin, August 21-26, 2011.
url: http://isi2011.congressplanner.eu/showabstract.php?congress=ISI2011&id=458.
The general actor-oriented model is explained, and an application is given to the like and dislike relation in Sampson's monastery data: a multivariate relation, of which one is positive and the other negative.

Longitudinal data of networks and behavior

Jin Wang (2007). Simulation Studies of Power and Robustness in Models for Network Dynamics.
MSc dissertation, Department of Statistics, University of Oxford.

Visualization

Ulrik Brandes, Natalie Indlekofer, and Martin Mader (2011). Visualization methods for longitudinal social networks and stochastic actor-oriented modeling. Social Networks, in press. DOI: http://dx.doi.org/10.1016/j.socnet.2011.06.002.

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.

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

Back to the main Siena page

	Articles about the SIENA program


	This webpage contains statistical and methodological papers about SIENA. Manual Ripley, Ruth M., Snijders, Tom A.B., and Preciado, Paulina. 2011. Manual for SIENA version 4.0. Oxford: University of Oxford, Department of Statistics; Nuffield College. For remaining users of Siena 3: 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); or the provisional Siena 3.2 manual.

	Introductory literature 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., van de Bunt, G.G., and Steglich, C.E.G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44-60. This is a tutorial. DOI: 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, pp. 215-247. 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., van de Bunt, G.G., and Steglich, C.E.G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44-60. This is a tutorial. DOI: http://dx.doi.org/10.1016/j.socnet.2009.02.004. Steglich, C.E.G., Snijders, T.A.B. and Pearson, M. (2010). Dynamic Networks and Behavior: Separating Selection from Influence. Sociological Methodology, 40, 329-393. DOI: http://dx.doi.org/10.1111/j.1467-9531.2010.01225.x . 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.

	*Literature with fundamental statistical description of the methods* Longitudinal network data Koskinen, Johan, and Edling, Christopher (2010). Modelling the evolution of a bipartite network - Peer referral in interlocking directorates. Social Networks, in press. DOI: http://dx.doi.org/10.1016/j.socnet.2010.03.001. Schweinberger, M., (2011). Statistical modeling of network panel data: Goodness of fit. British Journal of Statistical and Mathematical Psychology, in press. DOI: http://dx.doi.org/10.1111/j.2044-8317.2011.02022.x. 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. DOI: http://dx.doi.org/10.1111/0081-1750.00099. 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., Koskinen, Johan, and Schweinberger, Michael (2010). Maximum Likelihood Estimation for Social Network Dynamics. Annals of Applied Statistics 4, 567-588. DOI: http://dx.doi.org/10.1214/09-AOAS313. arXiv: http://arxiv.org/abs/1011.1753. 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.

	*Further literature about special topics* 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. Joshua A. Lospinoso, Michael Schweinberger, Tom A. B. Snijders and Ruth M. Ripley (2011). Assessing and accounting for time heterogeneity in stochastic actor oriented models.. Advances in Data Analysis and Classification, 5, 147-176. DOI: http://dx.doi.org/10.1007/s11634-010-0076-1. 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. Snijders, Tom A.B. (2011). Statistical models for dynamics of social networks: inference and applications. Proceedings, 50th Congress of the International Statistical Institute, Dublin, August 21-26, 2011. url: http://isi2011.congressplanner.eu/showabstract.php?congress=ISI2011&id=458. The general actor-oriented model is explained, and an application is given to the like and dislike relation in Sampson's monastery data: a multivariate relation, of which one is positive and the other negative. Longitudinal data of networks and behavior Jin Wang (2007). Simulation Studies of Power and Robustness in Models for Network Dynamics. MSc dissertation, Department of Statistics, University of Oxford. Visualization Ulrik Brandes, Natalie Indlekofer, and Martin Mader (2011). Visualization methods for longitudinal social networks and stochastic actor-oriented modeling. Social Networks, in press. DOI: http://dx.doi.org/10.1016/j.socnet.2011.06.002. 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.

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



	Back to the main Siena page