| Member Profile : Tom Snijders |
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 | Contact Information | Address: -Map Me-
Tom Snijders
University of Oxford, Dept. of Statistics
Nuffield College
New Road
Oxford, Oxfordshire, United Kingdom OX1 1NF
Phone : 0031651980109
E-mail : tom.snijders@nuffield.ox.ac.uk
Website : http://www.stats.ox.ac.uk/~snijders/
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| Bibliographic Information |
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| | | Software & Data | | Active Calendar Listings |
| Siena - statistical software for network dynamics | (Software) |
SIENA is a program for the statistical analysis of longitudinal social networks, including dynamics of networks and behavior. It is implemented as a package in the statistical system R; the package is called RSiena.
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 program, being an R package, is open source. The website contains extensive documentation and examples. |
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| | | Network Graduate Programs | | Network Courses |
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| | | Jobs Posted | | Sunbelt Submissions |
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| Sunbelt XXIX - March 10 to March 15, 2009 - Bahia Hotel | | Abstract : Analyzing the joint dynamics of several networks |
Network analysis traditionally focuses on single networks where ties are binary (on/off). This goes to the point where researchers may sometimes talk about “the” network, and reflect too little about the network content. This presentation is about actor-based models for analy\ing the joint dynamics of several interdependent networks on the same group of social actors. Examples are the joint dynamics of friendship and collaboration relations in a task-oriented group; or, as an example of the combination of a one-mode with a two-mode network, the joint dynamics of friendship and memberships in associations. One example of processes leading to interdependence between networks is social exchange, where actor i gives advice to j, while j gives esteem to i. Another example of processes is mixed transitive closure, where actors i and j are friends of k, and k refers i to j for advice.
The method for analysis is developed along the lines of stochastic actor-based models, where the several networks jointly determine the probabilities of tie changes, and where the paradigm of statistical inference is followed. This makes sense when the networks can be regarded as enduring but changeable relational states. The models proposed will be implemented in a future release of the SIENA program. |
| Workshop : The Analysis of Longitudinal Social Network Data using SIENA - Part 1 |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, as described in Snijders (2005) and Snijders, Steglich & Schweinberger (2006), and implemented in the SIENA program. The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution. An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in SIENA.
Further information about this method, including references and a JAVA demo, can be found at the SIENA website (see below). The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program SIENA is included in the StOCNET package which runs under Windows. The workshop will demonstrate the basics of using StOCNET and SIENA.
Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (3a, Tuesday afternoon) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (3b, Wednesday morning) is intended for those with previous experience with the method and the software, and also for those who followed the first session. will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, structurally determined values, and models for nondirected relations.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with StOCNET already installed, such that some steps of data manipulation and analysis can be followed hands-on.
SIENA website: http://stat.gamma.rug.nl/siena.html |
| Workshop : The Analysis of Longitudinal Social Network Data using SIENA - Part 1 & 2 |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, as described in Snijders (2005) and Snijders, Steglich & Schweinberger (2006), and implemented in the SIENA program. The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution. An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in SIENA.
Further information about this method, including references and a JAVA demo, can be found at the SIENA website (see below). The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program SIENA is included in the StOCNET package which runs under Windows. The workshop will demonstrate the basics of using StOCNET and SIENA.
Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (3a, Tuesday afternoon) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (3b, Wednesday morning) is intended for those with previous experience with the method and the software, and also for those who followed the first session. will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, structurally determined values, and models for nondirected relations.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with StOCNET already installed, such that some steps of data manipulation and analysis can be followed hands-on.
SIENA website: http://stat.gamma.rug.nl/siena.html |
| Workshop : The Analysis of Longitudinal Social Network Data using SIENA - Part 2 |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, as described in Snijders (2005) and Snijders, Steglich & Schweinberger (2006), and implemented in the SIENA program. The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution. An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in SIENA.
Further information about this method, including references and a JAVA demo, can be found at the SIENA website (see below). The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program SIENA is included in the StOCNET package which runs under Windows. The workshop will demonstrate the basics of using StOCNET and SIENA.
Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (3a, Tuesday afternoon) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (3b, Wednesday morning) is intended for those with previous experience with the method and the software, and also for those who followed the first session. will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, structurally determined values, and models for nondirected relations.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with StOCNET already installed, such that some steps of data manipulation and analysis can be followed hands-on.
SIENA website: http://stat.gamma.rug.nl/siena.html |
| SunBelt XXVIII - January 22 to January 27, 2008 - Trade Winds Beach Resort
http://www.tradewindsresort.com/
St. Pete Beach | | Workshop : 3a. Introduction to models for network dynamics and working with the SIENA program |
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| Workshop : 3a+b. Both models for dynamics of networks and behavior sessions. Tues 22nd, 1:00pm - 5:00pm and Wed 23rd, 9:00am - noon |
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| Workshop : 3b. Models for dynamics of networks and behavior; and other more advanced topics. |
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| Sunbelt XXX - June 29 to July 04, 2010 - Riva del Garda Fierecongressi | | Workshop : The Analysis of Longitudinal Social Network Data using SIENA - Part 1 ONLY |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2009), Introduction to actor-based models for network dynamics (in press, Social Networks), and implemented in the RSIENA program.
The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution. An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in RSIENA. Further information about this method can be found at the SIENA website (see below). The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSIENA is a package in the statistical computer system R.
The workshop will demonstrate the basics of using RSIENA. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (part a, Tuesday afternoon) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (part b, Wednesday morning) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with R and RSIENA already installed, such that some steps of data manipulation and analysis can be followed hands-on.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena |
| Workshop : The Analysis of Longitudinal Social Network Data using SIENA - Part 2 ONLY |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2009), Introduction to actor-based models for network dynamics (in press, Social Networks), and implemented in the RSIENA program.
The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution. An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in RSIENA. Further information about this method can be found at the SIENA website (see below). The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSIENA is a package in the statistical computer system R.
The workshop will demonstrate the basics of using RSIENA. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (part a, Tuesday afternoon) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (part b, Wednesday morning) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with R and RSIENA already installed, such that some steps of data manipulation and analysis can be followed hands-on.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena |
| Workshop : The Analysis of Longitudinal Social Network Data using SIENA - Parts 1 & 2 |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2009), Introduction to actor-based models for network dynamics (in press, Social Networks), and implemented in the RSIENA program.
The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution. An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in RSIENA. Further information about this method can be found at the SIENA website (see below). The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSIENA is a package in the statistical computer system R.
The workshop will demonstrate the basics of using RSIENA. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (part a, Tuesday afternoon) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (part b, Wednesday morning) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with R and RSIENA already installed, such that some steps of data manipulation and analysis can be followed hands-on.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena |
| Sunbelt XXVII - May 01 to May 06, 2007 - Corfu Island | | Workshop : 6. Tom Snijders, Christian Steglich The Analysis of Longitudinal Social Network Data using SIENA (starts Tues, May 1, 1:00; ends noon Wed May 2) |
| 6. Tom Snijders, Christian Steglich The Analysis of Longitudinal Social Network Data using SIENA (starts Tues, May 1, 1:00; ends noon Wed May 2) |
| Sunbelt XXXI - February 08 to February 13, 2011 - Trade Winds Beach Resort
http://www.tradewindsresort.com/
St. Pete Beach | | Workshop : The Analysis of Longitudinal Social Network Data using SIENA - PART 1 ONLY |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010), Introduction to actor-based models for network dynamics (Social Networks), and implemented in the RSiena program.
The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution.
An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in RSiena. Further information about this method can be found at the SIENA website (see below).
The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSiena is a package in the statistical computer system R. The workshop will demonstrate the basics of using RSiena. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (part a) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (part b) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with R and RSiena already installed, such that some steps of data manipulation and analysis can be followed hands-on. It will be helpful for participants to know how to run R and how to give basic R commands on their machine; but further knowledge of R is not required.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena |
| Workshop : The Analysis of Longitudinal Social Network Data using SIENA - PART 2 ONLY |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010), Introduction to actor-based models for network dynamics (Social Networks), and implemented in the RSiena program.
The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution.
An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in RSiena. Further information about this method can be found at the SIENA website (see below).
The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSiena is a package in the statistical computer system R. The workshop will demonstrate the basics of using RSiena. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (part a) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (part b) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with R and RSiena already installed, such that some steps of data manipulation and analysis can be followed hands-on. It will be helpful for participants to know how to run R and how to give basic R commands on their machine; but further knowledge of R is not required.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena |
| Workshop : The Analysis of Longitudinal Social Network Data using SIENA - PARTS 1 & 2 |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010), Introduction to actor-based models for network dynamics (Social Networks), and implemented in the RSiena program.
The statistical model used for the network evolution allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates. One interpretation of this model is an actor-oriented model where the nodes are actors whose choices determine the network evolution.
An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics of networks and behavior, which are a special option in RSiena. Further information about this method can be found at the SIENA website (see below).
The statistical analysis is based on Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSiena is a package in the statistical computer system R. The workshop will demonstrate the basics of using RSiena. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (part a) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (part b) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring an own laptop with R and RSiena already installed, such that some steps of data manipulation and analysis can be followed hands-on. It will be helpful for participants to know how to run R and how to give basic R commands on their machine; but further knowledge of R is not required.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena |
| Sunbelt XXXII - March 12 to March 18, 2012 - Crowne Plaza Hotel
https://resweb.passkey.com/go/INSNASunbelt
Redondo Beach | | Workshop : The Analysis of Longitudinal Social Network Data using SIENA (Part I & 2) |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010), Introduction to actor-based models for network dynamics (Social Networks), and implemented in the RSiena program.
The statistical model is the actor-oriented model where the nodes are actors whose choices determine the network evolution. This allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates.
An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics (‘co-evolution’) of networks and behavior, which are a special option in RSiena. Further information about this method can be found at the SIENA website (see below).
The statistical analysis is based on many repeated Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSiena is a package in the statistical computer system R. The workshop will demonstrate the basics of using RSiena. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (Part I) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (Part II) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring one’s own laptop with R and RSiena already installed, such that some steps of data manipulation and analysis can be followed hands-on. It will be helpful for participants to know how to run R and how to give basic R commands on their machine; but further knowledge of R is not required.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena
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| Workshop : The Analysis of Longitudinal Social Network Data using SIENA (part I) |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010), Introduction to actor-based models for network dynamics (Social Networks), and implemented in the RSiena program.
The statistical model is the actor-oriented model where the nodes are actors whose choices determine the network evolution. This allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates.
An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics (‘co-evolution’) of networks and behavior, which are a special option in RSiena. Further information about this method can be found at the SIENA website (see below).
The statistical analysis is based on many repeated Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSiena is a package in the statistical computer system R. The workshop will demonstrate the basics of using RSiena. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (Part I) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (Part II) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring one’s own laptop with R and RSiena already installed, such that some steps of data manipulation and analysis can be followed hands-on. It will be helpful for participants to know how to run R and how to give basic R commands on their machine; but further knowledge of R is not required.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena
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| Workshop : The Analysis of Longitudinal Social Network Data using SIENA (part II) |
This workshop is about statistical inference for longitudinal observations on social networks. Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 30 and a few hundred nodes). The workshop teaches the statistical method to analyze such data, for which a tutorial is given in Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G. (2010), Introduction to actor-based models for network dynamics (Social Networks), and implemented in the RSiena program.
The statistical model is the actor-oriented model where the nodes are actors whose choices determine the network evolution. This allows to include various network effects (reciprocity, transitivity, cycles, popularity, etc.), effects of individual covariates (covariates connected to the sender, the receiver, or the similarity between sender and receiver), and of dyadic covariates.
An important extension is to have, in addition to the network, one or more actor variables that evolve in mutual dependence with the network; an example is a friendship network of adolescents where drinking behavior is a relevant actor variable which influences, and is influenced by, the friendship network. This leads to models for the simultaneous dynamics (‘co-evolution’) of networks and behavior, which are a special option in RSiena. Further information about this method can be found at the SIENA website (see below).
The statistical analysis is based on many repeated Monte Carlo simulations of the network evolution model and therefore is a bit time-consuming. The computer program RSiena is a package in the statistical computer system R. The workshop will demonstrate the basics of using RSiena. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software.
The first session (Part I) is intended for those without previous experience with this method, and will focus on the intuitive understanding of the model and operation of the software. The second session (Part II) is intended for those with previous experience with the method and the software, and also for those who followed the first session. It will present models for the simultaneous dynamics of networks and behavior and other more advanced topics such as model specification, multivariate networks, structurally determined values, and goodness of fit checking.
Participants are requested to check the SIENA website in the week before the workshop to download the workshop materials. For optimal benefit, it is advisable to bring one’s own laptop with R and RSiena already installed, such that some steps of data manipulation and analysis can be followed hands-on. It will be helpful for participants to know how to run R and how to give basic R commands on their machine; but further knowledge of R is not required.
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena
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