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SIENA is a program for the statistical analysis of network data, with the focus on social networks.
Networks here are understood as entire (complete) networks, not as personal (egocentered) networks: it is assumed that a set of nodes (social actors) is given, and all ties (links) between these nodes are known - except perhaps for a moderate amount of missing data.
SIENA is designed for analyzing various types of data as dependent variables:

Longitudinal network data:: This refers to repeated measures of networks on a given node set (although it is allowed that there are some changes in the node set). Models can be specified with actor-oriented as well as tie-oriented dynamics; but mainly the former.
Longitudinal data of networks and behavior:: This is like longitudinal network data, but in addition there are one or more changing nodal variables that are also treated as dependent variables, and referred to as behavior. The network will influence the dynamics of the behavior, and the behavior will influence the dynamics of the network. In other words, this is about the co-evolution of networks and behavior.
Cross-sectional network data.: 'Cross-sectional' means that only one observation is available. This method uses exponential random graph models ('ERGMs'), also called p* models.
The ERG model is implemented in SIENA version 3, but not any more in version 4 (RSiena). It is still available, but no longer maintained.

The name SIENA stands for Simulation Investigation for Empirical Network Analysis. The method is implemented in the R package RSiena, and the more experimental version RSienaTest.

The main approach used by SIENA for modeling dynamics of network (or of networks and behavior) is an actor-oriented model, in which it is assumed that the social actors who are represented by the nodes in the network play a crucial role in changing their ties to other actors; in the case of associated behavior dynamics, also in changing their behavior. All of these models are Markov chain models; such models are more applicable to relations and behavioral variables that can be regarded as states than to relations or behavior that are more adequately regarded as non-enduring events.

The statistical analysis in SIENA is done on the basis of computer simulation of the network. This can be time consuming. In view of the detailed approach to network dynamics and the required computing resources, the method is applicable in principle to networks on approximately 10 to 1,000 nodes. For multiple replications of smaller networks (down to 4 or 5 nodes), a multilevel option sienaBayes() is (still experimentally) available in RSienaTest.

RSiena is hosted at GitHub as https://github.com/stocnet/rsiena/.

For requests of new effects or other new features, bug reports, etc. please open an issue at github.

A scientific summary is given below. The methods implemented in SIENA are described in the papers given in the webpage with literature. Many further articles with applications are given in the webpage with further applications. The program is obtained as a package within R and the extensive manual is downloadable here.

Users' group

There exists a Users' Group for SIENA to exchange information and seek technical advice.
The address is https://groups.io/g/RSiena.

If you are not a member yet, you can log in or sign up at the top of the page of the User Group.

You can view the archives of the RSiena group at groups.io.

SIENA version 4, also called RSiena, is a contributed package for the R statistical system, see https://cran.r-project.org/package=RSiena. It can be installed in the regular way from CRAN, but often a more recent version is obtainable as indicated at the downloads page.
The incorporation of RSiena in R makes available all other possibilities offered by R; in particular, to execute R in a Mac, Unix/Linux, or Solaris environment. Further information on RSiena is on the RSiena page.

RSiena was the recipient of the 2017 INSNA William D Richards Award. This is a "lifetime achievement award" for "publically available social network analysis software without which it would be impossible to study social networks".

Known bugs and new papers are given at the news webpage.

The SIENA program is part of an ongoing research effort. The research team is composed of Tom Snijders, Christian Steglich, Johan Koskinen, Nynke Niezink, Viviana Amati, Christoph Stadtfeld, and Stepan Zaretckii, with earlier contributions from Ruth Ripley, Krists Boitmanis, Felix Schönenberger, Charlotte Greenan, Josh Lospinoso, Paulina Preciado, Robert Krause, Michael Schweinberger, Mark Huisman, and Marijtje van Duijn. Courses and a user group are mentioned at the activities webpage.

The earlier SIENA 3 program also contains methods for analyzing Exponential Random Graph Models (ERGMs). For the latter methods, users now are referred to the standalone program pnet or the R package statnet, although the old SIENA version 3 still is available for those who wish to use it. It runs under Windows. The StOCNET interface still can be downloaded, but is no longer maintained.
SIENA 3 can be executed from the StOCNET environment, or as a stand alone program. The StOCNET project was an activity of Christian Steglich, Tom Snijders, and SciencePlus (Minne Oostra), with important earlier contributions from Evelien Zeggelink, Peter Boer, Bert Straatman, Mark Huisman, and others.

Scientific Summary

Social networks are dynamic by nature. Network dynamics is important for domains ranging from friendship networks (e.g., van Duijn et al., 2003; Burk et al., 2007; special issue of Journal of Research on Adolescence on Network and Behaviour Dynamics in Adolescence) to, for example, interorganisational networks (Borgatti and Foster, 2003; Berardo and Scholz, 2010). Ties can be established and can be terminated; also there may be changes in the node set. Changes in ties may be considered the result of the structural positions of the actors within the network - e.g., when friends of friends become friends -, characteristics of the actors ('actor covariates'), characteristics of pairs of actors ('dyadic covariates'), and residual random influences representing unexplained influences. The study of network dynamics should shed light on the underlying theoretical micro-mechanisms that induce the evolution of social network structures on the macro-level.
Stochastic actor-based models for network dynamics (Snijders, 2017; Snijders, van de Bunt, and Steglich, 2010) are a type of models that have the purpose to represent network dynamics on the basis of observed longitudinal data, and evaluate these according to the paradigm of statistical inference. This means that the models represent network dynamics as being driven by many different tendencies, such as the micro-mechanisms alluded to above, which may have been theoretically derived and/or empirically established in earlier research, and which may well operate simultaneously. Some examples of such tendencies are reciprocity, transitivity ('friends of my friends are my friends'), homophily (choice of network ties based on similarity of salient attributes), and assortative matching (choice of network ties based on similarity of network position). In this way, the models aim to give a good representation of the stochastic dependence between the creation, and possibly termination, of different network ties. These stochastic actor-based models allow to test hypotheses about these tendencies, and to estimate parameters expressing their strengths, while controlling for other tendencies (which in statistical terminology might be called 'confounders'). The actor-oriented nature means that changes in the network are modelled as representing from choices by the actors who are represented by the nodes in the network. This leads to a model combining agency and structure, and is well suited for expressing theories based on purposeful behaviour by social actors. With respect to data requirements, the focus is on the analysis of network panel data, i.e., data where the dependent variables are constituted by a network, and possibly one or more individual variables, that have been observed repeatedly for a given group of actors. The number of repeated measurements varies in practice usually from 2 to 10, but may be larger.
In the basic model (Snijders, 2001) the changing network is the dependent variable. Many important scientific questions, however, can be framed as questions about the mutually dependent dynamics of networks and attributes (behaviour, attitudes, performance, etc.) of the individual actors in the network. This here is called co-evolution of networks and behaviour, where the term 'behaviour' also stands for performance, attitudes, etc. The choice of network ties may depend on the attributes and network embeddedness of the actors and of their possible candidates towards whom to extend a tie; this is called social selection. Also, the behaviour of actors may depend on not only their own attributes but also on their network position and on the behaviour and other attributes of those to whom they are directly or indirectly tied in the network; this is called social influence. Models for the co-evolution of networks and behaviour allow the joint representation of social selection and social influence, as elaborated in Steglich, Snijders, and Pearson (2010).
In addition to models for one-mode networks, also models for two-mode networks (sometimes called affiliation networks), and for multivariate networks are implemented (Snijders, Lomi, and Torló, 2013). The latter also are co-evolution models. Thus it is possible to study, for example, the co-evolution of friendship and club membership.
Statistical methods for parameter estimation and hypothesis testing based on these models have been implemented in the R package RSiena (Ripley, Snijders, Boda, Vörös, and Preciado, 2017). R is a general statistical software system (R Development Core Team, 2017), and the incorporation of these methods in an R package allows combining them with many other statistical methods.

References (further see the webpage with statistical literature and the webpage with applications).

Berardo, R., and Scholz, J.T. (2010), Self-Organizing Policy Networks: Risk, Partner Selection and Cooperation in Estuaries. American Journal of Political Science, 54: 632-649.
Borgatti, S.P., Foster, P.C., 2003. The network paradigm in organizational research: a review and typology. Journal of Management, 29: 991-1013.
Burk, W.J., Steglich, C.E.G., and Snijders, T.A.B. (2007) Beyond dyadic interdependence: Actor-oriented models for co-evolving social networks and individual behaviors, International Journal of Behavioral Development, 31: 397-404.
Journal of Research on Adolescence, 23, Issue 3, pages 399-603.
Special Issue: Network and Behavior Dynamics in Adolescence.
R Development Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
Ruth 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, http://www.stats.ox.ac.uk/~snijders/siena/.
Tom A.B. Snijders (2001) The statistical evaluation of social network dynamics, Sociological Methodology - 2001, 40: 361-395.
Tom A.B. Snijders (2017). Stochastic Actor-Oriented Models for Network Dynamics. Annual Review of Statistics and Its Application, 4, 343-363.
DOI: http://dx.doi.org/10.1146/annurev-statistics-060116-054035
Here is the e-print access to this article.
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.
Snijders, T.A.B., Steglich, C.E.G., and Schweinberger, M. (2007) 'Modeling the co-evolution of networks and behavior', in Kees van Montfort, Han Oud and Albert Satorra (eds), Longitudinal Models in the Behavioral and Related Sciences. Mahwah, NJ: Lawrence Erlbaum. pp. 41-71.
Snijders, T.A.B., van de Bunt, G.G. and Steglich, C.E.G. (2010) Introduction to stochastic actor-based models for network dynamics, Social Networks, 32: 44-60.
van Duijn, M.A.J., Zeggelink, E.P.H., Huisman, M., Stokman, F.N., and Wasseur, F.W. (2003) Evolution of sociology freshmen into a friendship network, Journal of Mathematical Sociology, 27: 153-191.

Back to the main Siena page

	The Siena webpage


	SIENA is a program for the statistical analysis of network data, with the focus on social networks. Networks here are understood as entire (complete) networks, not as personal (egocentered) networks: it is assumed that a set of nodes (social actors) is given, and all ties (links) between these nodes are known - except perhaps for a moderate amount of missing data. SIENA is designed for analyzing various types of data as dependent variables: Longitudinal network data: This refers to repeated measures of networks on a given node set (although it is allowed that there are some changes in the node set). Models can be specified with actor-oriented as well as tie-oriented dynamics; but mainly the former. Longitudinal data of networks and behavior: This is like longitudinal network data, but in addition there are one or more changing nodal variables that are also treated as dependent variables, and referred to as behavior. The network will influence the dynamics of the behavior, and the behavior will influence the dynamics of the network. In other words, this is about the co-evolution of networks and behavior. Cross-sectional network data. 'Cross-sectional' means that only one observation is available. This method uses exponential random graph models ('ERGMs'), also called p* models. The ERG model is implemented in SIENA version 3, but not any more in version 4 (RSiena). It is still available, but no longer maintained. The name SIENA stands for Simulation Investigation for Empirical Network Analysis. The method is implemented in the R package RSiena, and the more experimental version RSienaTest. The main approach used by SIENA for modeling dynamics of network (or of networks and behavior) is an actor-oriented model, in which it is assumed that the social actors who are represented by the nodes in the network play a crucial role in changing their ties to other actors; in the case of associated behavior dynamics, also in changing their behavior. All of these models are Markov chain models; such models are more applicable to relations and behavioral variables that can be regarded as states than to relations or behavior that are more adequately regarded as non-enduring events. The statistical analysis in SIENA is done on the basis of computer simulation of the network. This can be time consuming. In view of the detailed approach to network dynamics and the required computing resources, the method is applicable in principle to networks on approximately 10 to 1,000 nodes. For multiple replications of smaller networks (down to 4 or 5 nodes), a multilevel option sienaBayes() is (still experimentally) available in RSienaTest. RSiena is hosted at GitHub as https://github.com/stocnet/rsiena/. For requests of new effects or other new features, bug reports, etc. please open an issue at github. A scientific summary is given below. The methods implemented in SIENA are described in the papers given in the webpage with literature. Many further articles with applications are given in the webpage with further applications. The program is obtained as a package within R and the extensive manual is downloadable here.

	Users' group There exists a Users' Group for SIENA to exchange information and seek technical advice. The address is https://groups.io/g/RSiena. If you are not a member yet, you can log in or sign up at the top of the page of the User Group. You can view the archives of the RSiena group at groups.io.

	SIENA version 4, also called RSiena, is a contributed package for the R statistical system, see https://cran.r-project.org/package=RSiena. It can be installed in the regular way from CRAN, but often a more recent version is obtainable as indicated at the downloads page. The incorporation of RSiena in R makes available all other possibilities offered by R; in particular, to execute R in a Mac, Unix/Linux, or Solaris environment. Further information on RSiena is on the RSiena page. RSiena was the recipient of the 2017 INSNA William D Richards Award. This is a "lifetime achievement award" for "publically available social network analysis software without which it would be impossible to study social networks". Known bugs and new papers are given at the news webpage. The SIENA program is part of an ongoing research effort. The research team is composed of Tom Snijders, Christian Steglich, Johan Koskinen, Nynke Niezink, Viviana Amati, Christoph Stadtfeld, and Stepan Zaretckii, with earlier contributions from Ruth Ripley, Krists Boitmanis, Felix Schönenberger, Charlotte Greenan, Josh Lospinoso, Paulina Preciado, Robert Krause, Michael Schweinberger, Mark Huisman, and Marijtje van Duijn. Courses and a user group are mentioned at the activities webpage. The earlier SIENA 3 program also contains methods for analyzing Exponential Random Graph Models (ERGMs). For the latter methods, users now are referred to the standalone program pnet or the R package statnet, although the old SIENA version 3 still is available for those who wish to use it. It runs under Windows. The StOCNET interface still can be downloaded, but is no longer maintained. SIENA 3 can be executed from the StOCNET environment, or as a stand alone program. The StOCNET project was an activity of Christian Steglich, Tom Snijders, and SciencePlus (Minne Oostra), with important earlier contributions from Evelien Zeggelink, Peter Boer, Bert Straatman, Mark Huisman, and others.

	Scientific Summary Social networks are dynamic by nature. Network dynamics is important for domains ranging from friendship networks (e.g., van Duijn et al., 2003; Burk et al., 2007; special issue of Journal of Research on Adolescence on Network and Behaviour Dynamics in Adolescence) to, for example, interorganisational networks (Borgatti and Foster, 2003; Berardo and Scholz, 2010). Ties can be established and can be terminated; also there may be changes in the node set. Changes in ties may be considered the result of the structural positions of the actors within the network - e.g., when friends of friends become friends -, characteristics of the actors ('actor covariates'), characteristics of pairs of actors ('dyadic covariates'), and residual random influences representing unexplained influences. The study of network dynamics should shed light on the underlying theoretical micro-mechanisms that induce the evolution of social network structures on the macro-level. Stochastic actor-based models for network dynamics (Snijders, 2017; Snijders, van de Bunt, and Steglich, 2010) are a type of models that have the purpose to represent network dynamics on the basis of observed longitudinal data, and evaluate these according to the paradigm of statistical inference. This means that the models represent network dynamics as being driven by many different tendencies, such as the micro-mechanisms alluded to above, which may have been theoretically derived and/or empirically established in earlier research, and which may well operate simultaneously. Some examples of such tendencies are reciprocity, transitivity ('friends of my friends are my friends'), homophily (choice of network ties based on similarity of salient attributes), and assortative matching (choice of network ties based on similarity of network position). In this way, the models aim to give a good representation of the stochastic dependence between the creation, and possibly termination, of different network ties. These stochastic actor-based models allow to test hypotheses about these tendencies, and to estimate parameters expressing their strengths, while controlling for other tendencies (which in statistical terminology might be called 'confounders'). The actor-oriented nature means that changes in the network are modelled as representing from choices by the actors who are represented by the nodes in the network. This leads to a model combining agency and structure, and is well suited for expressing theories based on purposeful behaviour by social actors. With respect to data requirements, the focus is on the analysis of network panel data, i.e., data where the dependent variables are constituted by a network, and possibly one or more individual variables, that have been observed repeatedly for a given group of actors. The number of repeated measurements varies in practice usually from 2 to 10, but may be larger. In the basic model (Snijders, 2001) the changing network is the dependent variable. Many important scientific questions, however, can be framed as questions about the mutually dependent dynamics of networks and attributes (behaviour, attitudes, performance, etc.) of the individual actors in the network. This here is called co-evolution of networks and behaviour, where the term 'behaviour' also stands for performance, attitudes, etc. The choice of network ties may depend on the attributes and network embeddedness of the actors and of their possible candidates towards whom to extend a tie; this is called social selection. Also, the behaviour of actors may depend on not only their own attributes but also on their network position and on the behaviour and other attributes of those to whom they are directly or indirectly tied in the network; this is called social influence. Models for the co-evolution of networks and behaviour allow the joint representation of social selection and social influence, as elaborated in Steglich, Snijders, and Pearson (2010). In addition to models for one-mode networks, also models for two-mode networks (sometimes called affiliation networks), and for multivariate networks are implemented (Snijders, Lomi, and Torló, 2013). The latter also are co-evolution models. Thus it is possible to study, for example, the co-evolution of friendship and club membership. Statistical methods for parameter estimation and hypothesis testing based on these models have been implemented in the R package RSiena (Ripley, Snijders, Boda, Vörös, and Preciado, 2017). R is a general statistical software system (R Development Core Team, 2017), and the incorporation of these methods in an R package allows combining them with many other statistical methods.
	References (further see the webpage with statistical literature and the webpage with applications). Berardo, R., and Scholz, J.T. (2010), Self-Organizing Policy Networks: Risk, Partner Selection and Cooperation in Estuaries. American Journal of Political Science, 54: 632-649. Borgatti, S.P., Foster, P.C., 2003. The network paradigm in organizational research: a review and typology. Journal of Management, 29: 991-1013. Burk, W.J., Steglich, C.E.G., and Snijders, T.A.B. (2007) Beyond dyadic interdependence: Actor-oriented models for co-evolving social networks and individual behaviors, International Journal of Behavioral Development, 31: 397-404. Journal of Research on Adolescence, 23, Issue 3, pages 399-603. Special Issue: Network and Behavior Dynamics in Adolescence. R Development Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. Ruth 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, http://www.stats.ox.ac.uk/~snijders/siena/. Tom A.B. Snijders (2001) The statistical evaluation of social network dynamics, Sociological Methodology - 2001, 40: 361-395. Tom A.B. Snijders (2017). Stochastic Actor-Oriented Models for Network Dynamics. Annual Review of Statistics and Its Application, 4, 343-363. DOI: http://dx.doi.org/10.1146/annurev-statistics-060116-054035 Here is the e-print access to this article. 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. Snijders, T.A.B., Steglich, C.E.G., and Schweinberger, M. (2007) 'Modeling the co-evolution of networks and behavior', in Kees van Montfort, Han Oud and Albert Satorra (eds), Longitudinal Models in the Behavioral and Related Sciences. Mahwah, NJ: Lawrence Erlbaum. pp. 41-71. Snijders, T.A.B., van de Bunt, G.G. and Steglich, C.E.G. (2010) Introduction to stochastic actor-based models for network dynamics, Social Networks, 32: 44-60. van Duijn, M.A.J., Zeggelink, E.P.H., Huisman, M., Stokman, F.N., and Wasseur, F.W. (2003) Evolution of sociology freshmen into a friendship network, Journal of Mathematical Sociology, 27: 153-191.




	Back to the main Siena page