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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 (2018).
Manual for SIENA version 4.0.
Oxford: University of Oxford, Department of Statistics; Nuffield College.
 Tom A.B. Snijders (2017).
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
 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.
 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
.
 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 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

The transparencies of the workshop The analysis of longitudinal social network data
held at European and Sunbelt Social Networks Conferences;
and a handout version of the same.
 The slides of the presentation Model Specification Recommendations for Siena, held at
the PRO meeting, Groningen, February 1, 2018, are
available here.
 Here is a YouTube video by
David Schaefer (UCI) :
https://www.youtube.com/watch?v=37WfzOP9Hw&index=2&list=UUtFhX_pi3WcfRQdG37u41gw
teaching at the
2017 Social Networks & Health Workshop
at Duke University about Stochastic Actororiented Models for Selection and Influence.
The slides are here.
 The slides of the Advanced Siena users' workshop
at the 2018 Sunbelt (Utrecht) can be obtained by
clicking here.
 The slides of the presentation Stochastic Actororiented Models
for Network: Basics and Coevolution, held at
Yale, January 24, 2017
available here.

The slides of the presentation by Tom Snijders
about recent developments in Siena, presented at the meeting of the European Collaborative
Research Project Social Influence in Dynamic Networks
(Paris, December 1518, 2014) are
available here.
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 February 2017).
Handout version of the same.
 Tom A.B. Snijders.
Analyzing the Joint Dynamics of Several Networks
(version March 2018)
Handout version of the same.
 Tom A.B. Snijders
Goodness of fit testing in RSiena (version January 2015).
 Specification of Homophily
in Actororiented Network Models, presentation by Tom Snijders at the
European Social Networks Conference, Paris, June 2016 (updated).
 Materials of the workshop on multilevel SAOM estimation
by the function sienaBayes can be consulted
here,




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.
 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.)
 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

 Per Block, Christoph Stadtfeld, and Tom A. B. Snijders (2016).
Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles.
Sociological Methods & Research, in press.
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."
 Schweinberger, M., and Snijders, T.A.B., (2007).
Markov models for digraph panel data: Monte Carlobased
derivative estimation.
Computational Statistics and Data Analysis 51, 44654483.
 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 Alessandro Lomi (2018).
Beyond Homophily:
Incorporating Actor Variables in Actororiented Network Models.
arXiv:1803.07172v1.
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.
Web:
https://arxiv.org/abs/1803.07172.
 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, in press / first view.
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.
 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.
 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).
Further work on such extensions is under way.
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 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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