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Articles about the SIENA program




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

Ruth M. Ripley, Tom A.B. Snijders, Zsófia Boda, Andras Vörös,
and Paulina Preciado (2020).
Manual for SIENA version 4.0.
Oxford: University of Oxford, Department of Statistics; Nuffield College.
 Tom A.B. Snijders (2019).
Siena algorithms.
This paper gives a sketch of the main algorithms used in RSiena.
It is meant as background material for understanding the code of
RSiena.
 For remaining users of Siena 3:
Tom A.B. Snijders, Christian E.G. Steglich, Michael Schweinberger, and Mark Huisman
(2010).
Manual for SIENA version 3.2.
University of Groningen: ICS / Department of Sociology;
University of Oxford: Department of Statistics, (2010).
(Note that Siena 3 is no longer supported, and any remaining users are being advised
to switch to RSiena! This program is still mentioned here for those
who wish to use its functionality for estimation of Exponential Random Graph Models).




Introductory literature
Also see the section 'Tutorial / review articles' on
the Siena applications page.
 Longitudinal network data

 Snijders, T.A.B., van de Bunt, G.G., and Steglich, C.E.G. (2010).
Introduction to actorbased models for network dynamics.
Social Networks, 32, 4460.
This is a tutorial.
DOI:
http://dx.doi.org/10.1016/j.socnet.2009.02.004.

Alberto Caimo and Nial Friel (2014).
ActorBased Models for Longitudinal Networks. Pp. 918 in
Reda Alhajj and Jon Rokne (eds.) Encyclopedia of Social Network Analysis and Mining.
New York: Springer.
DOI:
http://dx.doi.org/10.1007/9781461461708_166.
 Tom A.B. Snijders (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. 215247.
 Tom A.B. Snijders, 2006.
Statistical Methods for Network Dynamics.
In: S.R. Luchini et al. (eds.), Proceedings of the XLIII Scientific Meeting,
Italian Statistical Society, pp. 281296. Padova: CLEUP.
(This is an introduction for statistically oriented researchers.)

Tom A.B. Snijders (2017) (first edition 2014).
Siena: Statistical Modeling of Longitudinal Network Data. Pp. 17181725 in
Reda Alhajj and Jon Rokne (eds.) Encyclopedia of Social Network Analysis and Mining.
New York: Springer.
DOI:
https://doi.org/10.1007/9781461471639_3121.
 Tom A.B. Snijders (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. 215247.
 Video presentations

 Longitudinal data of networks and behavior

 Tom A.B. Snijders, Gerhard G. van de Bunt, and Christian E.G. Steglich (2010).
Introduction to actorbased models for network dynamics.
Social Networks, 32, 4460.
This is a tutorial.
DOI:
http://dx.doi.org/10.1016/j.socnet.2009.02.004
 Christian E.G. Steglich, Tom A.B. Snijders, and Michael Pearson (2010).
Dynamic Networks and Behavior: Separating Selection from Influence.
Sociological Methodology, 40, 329393.
DOI:
http://dx.doi.org/10.1111/j.14679531.2010.01225.x
 Yuval Kalish (2020). Stochastic ActorOriented
Models for the CoEvolution of Networks and Behavior:
An Introduction and Tutorial.
Organizational Research Methods, 23, 511534.
DOI:
http://dx.doi.org/10.1177/1094428118825300
 Tom A.B. Snijders (2009).
Longitudinal Methods of Network Analysis .
Pp. 59986013 in
Encyclopedia of Complexity and System Science (editorinchief Bob Meyers),
part of the Social Networks section (section editor John Scott), Springer Verlag, 2009.

René Veenstra, Jan Kornelis Dijkstra, Christian Steglich
and Maarten H. W. Van Zalk (2013).
NetworkBehavior Dynamics.
Journal of Research on Adolescence, 23, 399412.
DOI:
http://dx.doi.org/10.1111/jora.12070
 More general reviews including actororiented models

 Per Block, Johan Koskinen, James Hollway, Christian Steglich, and
Christoph Stadtfeld (2017).
Change we can believe in: Comparing longitudinal network models on consistency,
interpretability and predictive power.
Social Networks, 52, 180191.
DOI:
http://dx.doi.org/10.1016/j.socnet.2017.08.001
 Tom Broekel, PierreAlexandre Balland, Martijn Burger, and Frank van Oort (2014).
Modeling knowledge networks in economic geography: a discussion of four methods.
The Annals of Regional Science 53, 423452.
DOI:
http://dx.doi.org/10.1007/s0016801406162
 Mark Lubell, John Scholz, Ramiro Berardo, and Garry Robins (2012).
Testing Policy Theory with Statistical Models of Networks.
Policy Studies Journal 40, 351374.
DOI:
http://dx.doi.org/10.1111/j.15410072.2012.00457.x
 Garry Robins, Jenny M. Lewis, and Peng Wang (2012).
Statistical Network Analysis for Analyzing Policy Networks.
Policy Studies Journal 40, 375401.
DOI:
http://dx.doi.org/10.1111/j.15410072.2012.00458.x
 Tom A.B. Snijders. (2011).
Statistical Models for Social Networks.
Annual Review of Sociology, 37, 129151.
DOI:
http://dx.doi.org/10.1146/annurev.soc.012809.102709
eprint:
http://www.annualreviews.org/doi/pdf/10.1146/annurev.soc.012809.102709
 Veenstra, R., and Dijkstra, J.K. (2011).
Transformations in Peer Networks. Chapter 7 (pp. 135154) in
B. Laursen and W.A. Collins (eds.),
Relationship Pathways: From Adolescence to Young Adulthood.
New York: Sage.
 Veenstra, R., and Steglich, C. (2012).
Actorbased model for network and behavior dynamics:
A tool to examine selection and influence processes.
Chapter 34 (pp. 598618) in B. Laursen, T. D. Little, and N. A. Card (Eds.),
Handbook of developmental research methods. New York: Guilford Press.
 In Chinese

 Tom A.B. Snijders (2018) [2011], . Chinese translation (2018)
for Chongqing University Press
of
Network Dynamics,
Chapter 33 (pp. 501513) in John Scott and Peter J. Carrington (eds.),
The SAGE Handbook of Social Network Analysis. London: Sage, 2011.
 In Dutch

 Huisman, Mark, and Snijders, Tom A.B. (2003), Een stochastisch model voor netwerkevolutie.
Nederlands Tijdschrift voor de Psychologie, 58, 182194.
 In French

 In German

 In Italian

 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, 151181.
A preprint is available here.




Presentations (teaching material)
General
Specific topics
 Christoph Stadtfeld, with Tom Snijders, Marijtje van Duijn, Christian Steglich:
Statistical Power in Longitudinal Network Studies.
 Snijders, Tom A.B. (2008).
Statistical modeling of dynamics of nondirected networks.
Presentation at the XXV International Sunbelt Social Networks Conference,
Redondo Beach (Los Angeles), February 1620. 2005. Revised version.
Handout version of the same.
 Tom A.B. Snijders.
Multilevel analysis of network dynamics (version August 2020).
Handout version of the same.
 Tom A.B. Snijders.
Analyzing the Joint Dynamics of Several Networks
(version August 2020)
Handout version of the same.
 Tom A.B. Snijders
Goodness of fit testing in RSiena (version August 2020).
 Beyond Homophily,
slides for
Tom A.B. Snijders and Alessandro Lomi (2019).
Beyond Homophily:
Incorporating Actor Variables in Statistical Network Models.
Network Science, 7, 119
See below for the abstract.
Associated with this paper is the
script SelectionTables.r
which is available from the
scripts page.
 Tom A.B. Snijders
Multilevel analysis of network dynamics
using sienaBayes (version September 2019).
 Tom A.B. Snijders
Missing data in RSiena.
 Robert Krause, Multiple Imputation for RSiena,
version January, 2019.
 Robert Krause and Anna Iashina, Multiple Imputation for RSiena  Network and Behavior;
version September, 2019.




Literature with fundamental statistical description of the methods
 Longitudinal network data

 Amati, Viviana, Schönenberger, Felix, and Snijders, Tom A.B. (2015).
Estimation of stochastic actororiented models for
the evolution of networks by generalized method of moments.
Journal de la Société Française de Statistique,
156, 140165.
(About estimation by the Generalized Method of Moments in function
sienacpp in RSienaTest.)
 Greenan, Charlotte C., (2015).
Diffusion of Innovations in Dynamic Networks.
Journal of the Royal Statistical Society, Series A, 178, 147166.
DOI:
http://dx.doi.org/10.1111/rssa.12054.
(About diffusion of innovations / event history analysis coevolving
with a network; implemented by special rate effects, such as totExposure,
for a nondecreasing actor variable.
These special rate effects represent social influence.)
 Huisman, Mark, and Snijders, Tom A.B., (2003).
Statistical
analysis of longitudinal network data with changing composition.
Sociological Methods & Research, 32, 253287.
(Representing changing composition by the method of joiners
and leavers, implemented using function sienaCompositionChange.)
 Indlekofer, Natalie, and Brandes, Ulrik, (2013).
Relative importance of effects in stochastic actororiented models.
Network Science, Vol. 1, Issue 3, 278304.
DOI:
http://dx.doi.org/10.1017/nws.2013.21.
(Relative importance of effects, a kind of effect size;
is implemented in function sienaRI.)
 Koskinen, Johan, and Edling, Christopher (2012).
Modelling the evolution of a bipartite network
 Peer referral in interlocking directorates.
Social Networks, 34, 309322.
DOI:
http://dx.doi.org/10.1016/j.socnet.2010.03.001.
(Evolution of twomode networks, obtained by specifying
type='bipartite' in sienaDependent.)
 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, 147176.
DOI:
http://dx.doi.org/10.1007/s1163401000761.
(This test for time heterogeneity is carried out using function
sienaTimeTest.)
 Nynke M. D. Niezink and Tom A. B. Snijders (2017).
Coevolution of Social Networks and Continuous Actor Attributes.
The Annals of Applied Statistics, 11, 19481973.
DOI:
http://dx.doi.org/10.1214/17AOAS1037.
 Nynke M. D. Niezink and Tom A. B. Snijders (2018).
ContinuousTime Modeling of Panel Data with Network Structure.
In: Kees van Montfort, Johan H.L. Oud, and Manuel C. Voelkle (eds),
Continuous Time Modeling in the Behavioral and Related Sciences,
pp. 111134.
DOI:
http://dx.doi.org/10.1007/9783319772196_5.
 Nynke M. D. Niezink, Tom A. B. Snijders, and Marijtje van Duijn (2019).
No longer discrete:
Modeling the dynamics of social networks and continuous behavior.
Sociological Methodology, 49, 295340.
DOI:
http://dx.doi.org/10.1177/0081175019842263
 Schweinberger, M. (2012).
Statistical modeling of network panel data: Goodness of fit.
British Journal of Statistical and Mathematical Psychology, 65, 263281.
DOI:
http://dx.doi.org/10.1111/j.20448317.2011.02022.x.
(Develops the scoretype test available in function siena07
by using fix=TRUE, test=TRUE in includeEffects.)

Snijders, Tom A. B. (1995).
Methods for Longitudinal Social Network Data: Review and Markov Process Models.
Pages 211227 of: E.Tiit, Kollo, T., and Niemi, H. (eds),
New Trends in Probability and Statistics.
Vol. 3: Multivariate Statistics and Matrices in Statistics.
Proceedings of the 5th Tartu Conference. Lithuania: TEV Vilnius.
(Published conference paper, leading to Snijders (1996) (see below).
The very first paper about the stochastic actororiented model.)

Snijders, Tom A.B. (1996)
Stochastic actororiented models for network change.
Journal of Mathematical Sociology, 21, 149172.
(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. (2001).
The statistical evaluation of social network dynamics.
Sociological Methodology, 31, 361395.
DOI:
http://dx.doi.org/10.1111/00811750.00099.
(The basic introduction of the Stochastic ActorOriented Model.)
 Snijders, Tom A.B. (2004).
Explained Variation in Dynamic Network Models.
Mathematiques, Informatique et Sciences Humaines /
Mathematics and Social Sciences, 168, 2004(4), p. 3141.
(Introduces an entropybased measure of explained variation/effect size,
available using function sienaRI.)
 Snijders, Tom A.B. (2013).
Variance Reduction in the RobbinsMonro procedure in RSiena.
Paper presented at the 9th UKSNA Conference,
Greenwich, June 2728, 2013.
(Explains variance reduction by regression of the simulated statistics
on the scores, and by the partial diagonalization of the approximate
Jacobian. These are the 'dolby' and 'diagonalize' options
in siena07. Shows, for some simulation examples,
the improvement in results of siena07.)
 Tom A.B. Snijders. (2017).
Stochastic ActorOriented Models for Network Dynamics.
Annual Review of Statistics and Its Application, 4, 343363.
DOI:
http://dx.doi.org/10.1146/annurevstatistics060116054035
Here is the eprint access to this article.
(General overview from a statistical point of view.)
 Snijders, Tom A.B., Koskinen, Johan, and Schweinberger, Michael (2010).
Maximum Likelihood Estimation for Social Network Dynamics.
Annals of Applied Statistics 4, 567588.
DOI:
http://dx.doi.org/10.1214/09AOAS313.
arXiv:
http://arxiv.org/abs/1011.1753.
(Explains the maximum likelihood procedure available in siena07,
used when maxlike=TRUE is specified in sienaAlgorithmCreate.)
 Snijders, Tom A.B. and Pickup, Mark (2017).
Stochastic ActorOriented Models for Network Dynamics.
Pp. 221247 in
Oxford Handbook of Political Networks, edited by
Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell.
Oxford: Oxford University Press.
DOI:
http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.10.
Also available in
OUP Handbooksonline.
(Gives a general introduction, and also explains the Stochastic ActorOriented
Model for nondirected networks.)
 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 , 493512. Berlin: Springer.
(Published conference paper, the first publication about the
Stochastic ActorOriented Model for binary networks.)
 Longitudinal data of networks and behavior





Further literature about special topics
 Longitudinal network data

 jimi Adams and David Schaefer (2018).
Visualizing Stochastic Actorbased Model Microsteps.
Socius, "Sociological Research for a Dynamic World", Sage Publications.
DOI:
https://doi.org/10.1177/2378023118816545
This visualization provides a dynamic representation of the microsteps
involved in modeling network and behavior change with a stochastic
actorbased model.
See the
script for executing this at the Siena scripts page.
 Per Block, Christoph Stadtfeld, and Tom A. B. Snijders (2019).
Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles.
Sociological Methods & Research, 48, 202239.
DOI:
http://dx.doi.org/10.1177/0049124116672680
 Koskinen, Johan H., and Snijders, Tom A.B., (2007).
Bayesian inference for dynamic social network data.
Journal of Statistical Planning and Inference, 137 (2007), 39303938.
DOI:
http://dx.doi.org/10.1016/j.jspi.2007.04.011.
 Lars Leszczensky, Harald Beier, Hanno Kruse and Sebastian Pink (2016).
Collecting network panel data in schools: Practical guidance based on
the experiences of three German research projects.
http://www.mzes.unimannheim.de/publications/wp/wp166.pdf
 Jürgen Lerner, Natalie Indlekofer, Bobo Nick, and Ulrik Brandes (2013).
Conditional independence in dynamic networks.
Journal of Mathematical Psychology, 57, 275283.
DOI:
http://dx.doi.org/10.1016/j.jmp.2012.03.002.
Compares conditional independence models with Temporal Exponential
Random Graph models and Stochastic ActorOriented Models.
"Our results suggest that conditional independence models are inappropriate
as a general model for network evolution and can lead to distorted substantive
findings on structural network effects, such as transitivity.
On the other hand, the conditional independence
assumption becomes less severe when interobservation times are relatively short."
 Josh A. Lospinoso and Tom A. B. Snijders (2019).
Goodness of fit for Stochastic ActorOriented Models.
Methodological Innovations, 12, 2059799119884282.
DOI:
https://doi.org/10.1177/2059799119884282.
The theoretical basis for the sienaGOF function.
 Schweinberger, Michael, and Snijders, Tom A.B., (2007).
Markov models for digraph panel data: Monte Carlobased
derivative estimation.
Computational Statistics and Data Analysis 51, 44654483.
The theoretical basis for the estimation of standard errors,
based on the score function.
 Schweinberger, Michael (2020).
Statistical inference for continuoustime Markov processes with block structure
based on discretetime network data.
Statistica Neerlandica 74, 342362.
DOI:
http://dx.doi.org/10.1111/stan.12196
This method is not contained in the RSiena package.
It is implemented in an extended version of the earlier Delphi code in
Siena 3, available from the author's website.
 Snijders, Tom A.B (2003).
Accounting for Degree Distributions
in Empirical Analysis of Network Dynamics.
Pp. 146161 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 outdegrees.
 Snijders, Tom A.B. (2011).
Statistical models for dynamics of social networks:
inference and applications.
Proceedings,
58th Congress of the International
Statistical Institute, Dublin, August 2126, 2011.
The general actororiented model is explained, and
an application is given to the like and dislike relation
in Sampson's monastery data: this is a signed graph
represented here as a multivariate relation,
of which one relation is positive and the other negative.
 Tom A.B. Snijders and Frank Kalter (2020).
Religious diversity and social cohesion in German classrooms;
A micromacro study based on empirical simulations.
In:
Advances in the sociology of trust and cooperation;
Theory, experiments, and field studies,
edited by Vincent Buskens, Rense Corten, and Chris Snijders; De Gruyter.
Open Access: PDF ISBN 9783110647495, EPUB ISBN 9783110647617.
Publication date: October, 2020.
This chapter proposes an integrated statistical approach to studying
the micromacro transition by combining a random coefficient multilevel
approach with the Stochastic ActorOriented Model.
This is elaborated for the substantively interesting and topical
question whether the growing ethnic and religious
diversity in our societies, along with the wellknown tendency
for homophily, necessarily lead to a decline in social cohesion.
 Tom A.B. Snijders and Alessandro Lomi (2019).
Beyond Homophily:
Incorporating Actor Variables in Statistical Network Models.
Network Science, 7, 119.
Next to homophily, three other mechanisms are discussed that may play
a role in how numerical actor attributes affect directed social networks:
aspiration, conformity, and sociability. A fourparameter specification
is proposed for the effects of numerical actor attributes on directed
social networks.
DOI:
http://dx.doi.org/10.1017/nws.2018.30
See slides above;
associated with this paper is the
script SelectionTables.r
which is available from the
scripts page.
 Tom A.B. Snijders and Christian E.G. Steglich (2015).
Representing MicroMacro Linkages by ActorBased Dynamic Network Models.
Sociological Methods & Research, 44, 222271.
Elaborates the relation between stochastic actororiented models
and agentbased simulation models.
DOI: http://dx.doi.org/10.1177/0049124113494573
 Design of longitudinal network studies, power issues

 Longitudinal data of networks and behavior

 Dynamic Exponential Random Graph Models

 Tom Snijders and Johan Koskinen, Longitudinal Models.
Chapter 11 (pp. 130140) in
Exponential Random Graph Models for Social Networks,
D. Lusher, J. Koskinen, and G. Robins (eds.),
Cambridge University Press, 2013.
 Coevolution of multiple networks

 Tom A.B. Snijders, Alessandro Lomi, and Vanina Jasmine Torló (2013).
A model for the multiplex dynamics of twomode and onemode networks,
with an application to employment preference, friendship, and advice.
Social Networks, 35, 265276.
DOI: http://dx.doi.org/10.1016/j.socnet.2012.05.005.
In Figure 2, Y_{ia} and Y_{ja} should be interchanged.
This paper presents stochastic actororiented models
for onemode / twomode network coevolution.
 Kayo Fujimoto, Tom A.B. Snijders, and Thomas W. Valente (2018).
Multivariate dynamics of onemode and twomode networks: Explaining similarity
in sports participation among friends. Network Science, 6, 370395.
DOI:
http://dx.doi.org/10.1017/nws.2018.11
This paper furter develops modeling of onemode / twomode network coevolution,
applied to the dynamic interplay between the twomode affiliation network of
adolescents' participation in sports activities and the onemode friendship network.
Methodologically it is about the two mechanisms for mixed transitive closure:
affiliationbased focal closure, where individuals participating in the same sport
become (or stay) friends; and associationbased affiliation closure, where
individuals start (or continue) participating in the sports in which their friends
are already active.
As a corresponding descriptive method generalizing the 'pie charts' of Steglich et al. (2010),
we propose a quantitative measure for the relative strength of these two mechanisms as explanations
of the association between the onemode and the twomode network.
A further methodological issue is that two specifications are
given to represent homophily effects in the twomode network.
 Missing data

 Kayla de la Haye, Joshua Embree, Marc Punkay, Dorothy L. Espelage,
Joan S. Tucker, and Harold D. Green Jr. (2017).
Analytic strategies for longitudinal networks with missing data.
Social Networks, 50, 1725.
DOI:
http://dx.doi.org/10.1016/j.socnet.2017.02.001
Proposes a strategy for dealing with varying actors subsets having missing data.
The strategy is based on the multiple groups option.
 Hipp, J.R., Wang, C., Butts, C.T., Jose, R., Lakon, C.M. (2015).
Research note: the consequences of different methods for handling
missing network data in stochastic actor based models.
Social Networks 41, 5671.
DOI:
http://dx.doi.org/10.1016/j.socnet.2014.12.004.
Note
This paper presents a method for handling missing data in longitudinal
network modeling that is better than the simple method employed in RSiena.
However, the method in RSiena is not as poor as these authors suggest.
The paper misrepresents details of the way in which RSiena handles missing data.
The writing suggests that the 'third strategy' mentioned on p. 57 is
the strategy employed by RSiena. This is not the case, because although
RSiena uses the imputation strategy as described here, it also excludes
the tie and behavior variables for which a data values is missing
at a wave and/or a preceding wave from the calculation of the
target statistics for this wave. Thus, the effect of the imputation
is minimized.
See Huisman and Steglich (2008, Section 4.4), and the RSiena Manual,
Section 4.3.2.
 Huisman, Mark, and Steglich, C.E.G., (2008).
Treatment of nonresponse in longitudinal network data.
Social Networks, 30, 297308.
 Robert W. Krause, Mark Huisman, and Tom A.B. Snijders (2018).
Multiple imputation for longitudinal network data.
Italian Journal of Applied Statistics, 30, 3357.
DOI: https://doi.org/10.26398/IJAS.0030002.
Missing data on network ties are a fundamental problem for network analysis.
In this paper, we present a new method with two variants to handle missing data
due to actor nonresponse in the framework of stochastic actororiented models (SAOMs).
The proposed method imputes missing tie variables in the first wave either by
using a Bayesian exponential random graph model (BERGMs) or a stationary SAOM
and imputes missing tie variables in later waves utilizing a SAOM.
The proposed method is compared to the standard SAOM missing data treatment
as well as recently proposed methods.
The multiple imputation procedure provided more reliable point estimates than the default treatment.
A script for this paper, and another one for related missing data issues,
is at the Siena scripts page.
 Cheng Wang, Carter T. Butts, John R. Hipp, Rupa Jose,
and Cynthia M. Lakon (2016).
Multiple imputation for missing edge data:
A predictive evaluation method with application to Add Health.
Social Networks 45, 8998.
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.12.003.
Note
See the note about the paper by Hipp et al. (2015), a few lines up.
This paper likewise misrepresents the way in which RSiena handles missing data.
The phrase 'it simply drops them or treats them as absent'
(p. 95) is incorrect: the imputation used in RSiena by 'last observation carried
forward' is somewhat more informative than this, and the exclusion
of the imputed data from the target statistics means that 'simply'
is not applicable. Further, 'As Hipp et al. (2015) found' is incorrect,
because Hipp et al. (2015) used a different imputation strategy
in their comparisons, as mentioned above.
 Tjeerd Zandberg and Mark Huisman (2019).
Missing behavior data in longitudinal network studies:
The impact of treatment methods on estimated effect parameters in stochastic
actor oriented models.
Social Network Analysis and Mining, 9.8.
DOI:
https://doi.org/10.1007/s1327801905532.
This paper examines seven different methods that are currently available
to deal with missing behavior data.
The effect of the missing data methods was inspected using three criteria:
model convergence, parameter bias, and parameter coverage.
The results show that, in general, the default method available in the
RSIENA software gives the best outcomes for all three criteria.
 Visualization

 Ulrik Brandes, Natalie Indlekofer, and Martin Mader (2012).
Visualization methods for longitudinal social networks and stochastic actororiented modeling.
Social Networks, 34, 291308.
DOI:
http://dx.doi.org/10.1016/j.socnet.2011.06.002.
 Multilevel dynamic network analysis and metaanalysis of network dynamics

 Nick Crossley, Elisa Bellotti, Gemma Edwards, Martin G. Everett,
Johan Koskinen and Mark Tranmer (2015).
Social Network Analysis for EgoNets. London: Sage.
See p. 171177 for a concise treatment of the use of Siena for the analysis
of longitudinal observations of multiple ego networks.

Tom A.B. Snijders and Chris Baerveldt,
A Multilevel Network Study of the Effects
of Delinquent Behavior on Friendship Evolution.
Journal of Mathematical Sociology 27 (2003), 123151.
This is about a 'multilevel' or 'metaanalysis' extension of this longitudinal method to the case
of observations on several networks.
It is implemented in the function siena08()
(see the script RscriptMultipleGroups.R on the
RSiena scripts page).
A set of transparencies explaining this method
(revised version, December 2013)
can be downloaded here.
The data are available from the
Siena data sets page.
 Tom A.B. Snijders (2016),
The Multiple Flavours of Multilevel Issues for Networks.
Chapter 2 in Emmanuel Lazega and Tom A.B. Snijders (eds.),
Multilevel Network Analysis for the Social Sciences,
Cham: Springer, 2016.
ISBN 9783319245188 ISBN 9783319245201 (eBook)
DOI: 10.1007/9783319245201
 Tom A.B. Snijders, Malick Faye, and Julien Brailly (2020).
Network dynamics with a nested node set: Sociability in seven villages in Senegal.
Statistica Neerlandica 74, 300323.
DOI: http://dx.doi.org/10.1111/stan.12208
From the abstract:
We propose two complementary ways to deal with a
nesting structure in the node set of a network; such
a structure may be called a multilevel network, with a
node set consisting of several groups. First, withingroup
ties are distinguished from betweengroup ties by considering
them as two distinct but interrelated networks.
Second, effects of nodal variables are differentiated
according to the levels of the nesting structure, to prevent
ecological fallacies.
 Tom A.B. Snijders and Johan Koskinen (2012).
Multilevel Longitudinal Analysis of Social Networks.
Paper presented at the 8th UKSNA Conference,
Bristol, June 2830, 2012.
 Weihua (Edward) An (2015).
Multilevel meta network analysis with application to studying network
dynamics of network interventions.
Social Networks, 43, 4856.
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.03.006.
See notes about this paper at the
News page.




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





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