SIENA news
Overview of availability of recent versions of packages
The newest "official" version of
The most recent version of
The most recent version of
April 24, 2024:
The main changes visible to users with respect to the earlier version 1.4.10 are:
- Effects:
- Effect name outOutDist2AvIntn changed to avAlt.2M.tot .
- New effects avAlt.2M.tie , avAlt.2M.tot , avAltU.2M.tie ,
dist2OutInActIntn , nDist2ActIntn , sharedToU .
These are especially useful for two-mode networks; hence the part .2M. in some of the names. - Changed sqrt treatment in outOutDist2ActIntn and outOutDist2AvIntn / avAlt.2M.tot (see the manual).
- Improvements of functionality:
- Method print.sienaEffects has an extra parameter includeShortNames
to do what the name of this parameter suggests.
If you like to be reminded of what are the shortNames of each effect in effects object myeff, use
print(myeff, includeShortNames=TRUE)
- Method print.sienaEffects has an extra parameter includeShortNames
to do what the name of this parameter suggests.
March 26, 2024:
The main changes visible to users with respect to the earlier version 1.4.8 are:
- New effects:
- New effects crossXOutAct, outOutDist2ActIntn, outOutDist2AvIntn, inPopOutW.
- New effects sameXV and sameXVInPop for bipartite and symmetric networks.
- sameXCycle4 added for one-mode and symmetric networks.
- sharedTo gets default internal effect parameter p=3 .
- Improvements of functionality:
- In
multiSiena : function simulateData, for forward simulations based on sienaBayes results, extended to allow multiple dependent variables. - Function updateSpecification now also updates interaction effects and initialValues.
- New parameter silent in sienaAlgorithmCreate activated.
- In
- Correction of memory leak that affected ML estimation and sienaBayes.
- New parameter silent in sienaAlgorithmCreate.
The new effects outOutDist2ActIntn, outOutDist2AvIntn, inPopOutW, sameXV, and sameXVInPop are of special interest for two-mode (bipartite) networks, and researchers who are studying such networks are invited to take a look in the manual for their description.
In particular:
Differences between sameXInPop, sameXVInPop, sameXCycle4:
All work with an actor variable
Both variables
All three effects express (for positive parameters) that actors with the same categories of
For sameXInPop, they tend to choose the same nodes.
For sameXVInPop, they tend to choose nodes of the same
February 29, 2024:
The main changes visible to users with respect to the earlier version 1.4.7 are:
- Correction of memory leak that affected ML estimation and sienaBayes.
- New parameter silent in sienaAlgorithmCreate.
February 22, 2024:
The main changes visible to users with respect to the earlier version 1.4.2 (various of them because of issues raised at groups.io/g/RSiena) are:
- New parameter targets in siena07, used to supersede the targets
calculated from the data (not for use in estimation for regular data sets,
see the help file for siena07).
The use of this parameter for simulations will be demonstrated in a script on the website. - Allow getEffects to construct effects of more than one dependent network on continuous behavior dependent variables.
- Some of the recently added effect groups were missing from effectsDocumentation. This led to an incomplete listing of the effects. They are now included.
- effectsDocumentation() reports to the console the name of the file that was written.
- sienaDataCreate stops with an error message if there is a bipartite network before a one-mode network.
- If the data contains a continuous dependent behavioral variable and the algorithm specifies conditional estimation, the estimation stops with a clear error message.
- Allow sienaDataCreate to work with a single variable defined as a dependent network given as a list of sparse matrices.
- Changed the check for constant dyadic covariates for sparse matrices (GitHub issue #88).
- sienaRI temporarily disabled because of a memory leak.
However, this only means it is not 'exported'; it is still available internally, and you still can use it as RSiena:::sienaRI. - The option prML=2 for ML estimation by siena07 temporarily disabled because of a memory leak in the move proposal distribution (sienaAlgorithmCreate).
February 19, 2024:
This means it is not available any more directly through install.packages().
It is still available from GitHub.
The reason for the archiving was a memory leak in sienaRI
and in maximum likelihood estimation by siena07 using prML=2
in sienaAlgorithmCreate.
This does not affect any other functionality.
I'm working on this and hope that
Repository
stocnet
also contains the R packages
December 14, 2023:
The main changes visible to users with respect to the earlier version 1.4.1 are:
- Correction of an error in the model for continuous dependent behavior variables.
- Effects:
- New effect
outMoreX . - Effect
outMore corrected and triplicated asoutMore2 andoutMore3 , to allow using it with several different internal effect parameters. - Interactiontype of
altLThresholdX andaltRThresholdX is dyadic; ofdegAbsDiffX ,degPosDiffX , anddegNegDiffX is "" (blank).
- New effect
- New parameter iterations in
sienaGOF to allow shorter computations.
November 1, 2023:
The change in version number from 1.4 to 1.3 is because of the cumulative changes
since 1.3.0, not because of particularly important differences with version 1.3.28.
The main change is the improved and extended citation available as
October 12, 2023:
The main changes visible to users with respect to the earlier GitHub version 1.3.26 are:
- Changed effects:
-
threshold ,threshold2 ,threshold3 ,threshold4 changed to work with internal effect parameters referring to the non-centered behavioral variable. Note that this is not backward-compatible; if you use these effects, you have to change the internal effect parameters!
-
- Improved documentation:
- Descriptions of effects
altInDist2W andtotInDist2W added to the manual (the effects had been there since a long time, but not documented).
- Descriptions of effects
This represents also the contributions from the threshold effects mentioned above.
October 3, 2023:
This represents also the contributions from the one-sided
similarity influence effects October 1, 2023:
New version 1.2.19 of
The main changes visible to users with respect to the earlier GitHub version 1.3.26 are:
August 21, 2023:
New version 1.2.18 of
The main changes visible to users with respect to the earlier GitHub version 1.3.23 are:
June 30, 2023:
New version 1.2.17 of
The main changes visible to users with respect to the earlier GitHub version 1.3.22 are:
May 13, 2023:
New version 1.2.16 of
The main changes visible to users with respect to the earlier GitHub version 1.3.20 are:
April, 2023:
New version 1.2.15 of
The main changes visible to users with respect to the earlier GitHub version 1.3.19 are:
Sometime early 2023:
Johan Koskinen and Tom A.B. Snijders (2023).
Multilevel longitudinal analysis of social networks. February 7, 2023:
It has the same functionality as version 1.3.14, except that problems were
corrected that led to errors on certain platforms
(related to the clang 16 C++ compiler).
This integrates version 1.3.18 with the recent changes of CRAN version 1.3.14 to 1.3.14.1
and has some further changes.
The main changes visible to users with respect to the earlier GitHub version 1.3.18 are:
January 30, 2023:
New version 1.2.14 of
The main changes visible to users with respect to the earlier GitHub version 1.3.13 are:
November 27, 2022:
New version 1.2.13 of
November 11, 2022:
New version 1.2.12 of
This version of October 11, 2022:
The main changes visible to users with respect to the earlier GitHub version 1.3.11 are:
June 1, 2022:
The main changes are:
May 11, 2022:
The main changes are:
March 22, 2022:
As a step toward integration of
March 18, 2022:
The main changes are:
March 14, 2022:
Per Block, James Hollway, Christoph Stadtfeld, Johan Koskinen and Tom Snijders (2022).
Circular specifications and "predicting" with information
from the future: Errors in the empirical SAOM-TERGM
comparison of Leifeld & Cranmer.
Abstract
We review the empirical comparison of Stochastic Actor-oriented Models (SAOMs)
and Temporal Exponential Random Graph Models (TERGMs) by Leifeld & Cranmer in
Network Science (2019). When specifying their TERGM,
they use exogenous nodal attributes calculated from the outcome networks' observed
degrees instead of endogenous ERGM equivalents of structural effects as used in the SAOM.
This turns the modeled endogeneity into circularity and obtained results are tautological.
In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample
information and thereby predict the future using observations from the future.
Thus, their analysis rests on erroneous model specifications that invalidate the article's
conclusions. Finally, beyond these specific points, we argue that their evaluation
metric - tie-level predictive accuracy - is unsuited for the task of comparing model performance.
March 8, 2022:
The statistical theory behind the sienaBayes function is now
available in a paper at the ArXiV:
Johan Koskinen and Tom A.B. Snijders (2022).
Multilevel longitudinal analysis of social networks.
arXiv preprint arXiv:2201.12713.
The main changes are:
February 17, 2022:
The main changes are:
December 15, 2021:
There was a bug in version 1.3.4, which would turn up when models
with interaction effects were estimated. This was corrected in version 1.3.5.
December 8, 2021:
The main changes are:
October 11, 2021:
The main changes are:
October 6, 2021:
Bug found in sienaGroupCreate: if there are any covariates that
are constant in some group, the simX effect will lead to an error
with error message "total probability non-positive".
This may be a nuisance especially when using sienaBayes.
This will be corrected in the new version of July 29, 2021:
The main changes are:
July 13, 2021:
This is again a very minor update of sienaBayes. The only change is:
May 10, 2021:
The cumulative importance of all changes in the past two years
(including Generalized Method of Moments and continuous
behavioral variables) justifies that the version number
now proceeds from 1.2.x to 1.3.0.
For changes with respect to earlier versions, see below, or look at the
NEWS.md file in the source code on GitHub, and
appendix B in the
Siena manual.
May 1, 2021:
Main changes :
April 15, 2021:
This is a very minor update of sienaBayes. The main changes are:
April 6, 2021:
For the behavior of structural zeros
there is a difference between the MoM and ML /
Structural zeros (indicated in the data by the code 10)
can be used to indicate turnover, i.e.,
actors entering or leaving the network. However, for ML estimation
and Summary: for estimation by ML or
What to do to represent change of composition for estimation by ML
or March 22, 2021:
Main changes :
Main changes :
March 17, 2021:
Main changes :
Generalized Method of Moments
The Generalized Method of Moments now is implemented
(thanks to Viviana Amati and Felix Schönenberger).
The software implementation is documented in GitHub at
Changes_RSiena_GMoM.pdf.
Effects:
Further improvements of functionality:
Documentation:
Reduction of functionality:
Corrections / safeguards:
March 5, 2021:
February 24, 2021:
Main changes : December 10, 2020:
This completes the transition of the RSiena package to GitHub.
The repository at R-Forge now is relevant only for RSienaTest.
Version 1.2-29 is a follow-up both of version 1.2-27 (R-Forge)
and 1.2-28 (GitHub).
Main changes from versions 1.2-27 and 1.2-28: November 10, 2020:
October 1, 2020:
Main changes: September 17, 2020:
This was carried out in collaboration with James Hollway.
The
release at GitHub contains version 1.2-26, and transition to version 1.2-27
(see below) will take place soon.
At GitHub, there is a
wiki
which is still in a phase of development.
September 16, 2020:
Main changes in RSiena and RSienaTest visible to users are the
following.
April 8, 2020:
Main changes in RSiena and RSienaTest visible to users are the
following.
February 16, 2020:
Main changes in RSiena and RSienaTest visible to users are the
following.
January 12, 2020:
Some changes in RSiena:
January 6, 2020:
Main changes in RSiena and RSienaTest visible to users are the
following.
November 11, 2019: Publication of a paper by Josh Lospinoso and Tom Snijders
about the basis of the sienaGOF function, including a data-driven way
for selecting effects that may improve fit when it is poor.
Josh A. Lospinoso and Tom A. B. Snijders (2019).
Goodness of fit for Stochastic Actor-Oriented Models.
Methodological Innovations, 12, 2059799119884282.
Abstract
We propose a Mahalanobis distance-based Monte Carlo goodness of
fit testing procedure for the family of stochastic actor-oriented
models for social network evolution.
A modified model distance estimator is proposed to help the researcher
identify model extensions that will remediate poor fit.
The content of this paper was present already in Josh's DPhil dissertation
of 2012, but was not yet published in a scientific journal.
November 5, 2019.
A paper to avoid confusion about the SAOM-TERGM comparison.
Per Block, James Hollway, Christoph Stadtfeld, Johan Koskinen and Tom Snijders (2019).
"Predicting" after peeking into the future:
Correcting a fundamental flaw in the SAOM - TERGM
comparison of Leifeld and Cranmer (2019).
ArXiV,
http://arxiv.org/abs/1911.01385.
Abstract
We review the empirical comparison of SAOMs and TERGMs by Leifeld &
Cranmer (2019) in Network Science. We note that their model
specification uses nodal covariates calculated from observed degrees
instead of using structural effects, thus turning endogeneity into
circularity. In consequence, their out-of-sample predictions using
TERGMs are based on out-of-sample information and thereby predict the
future using observations from the future. We conclude that their
analysis rest on erroneous model specifications that render the
article's conclusions meaningless. Consequently, researchers should
disregard recommendations from the criticized paper when making
informed modelling
choices.
May 21, 2019:
Main changes in RSiena and RSienaTest visible to users are the
following.
May 10, 2019: Publication of a paper by Nynke Niezink and co-authors
explaining the extension of the Stochastic Actor-oriented Model to
continuous behavior variables.
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.
Abstract
The dynamics of individual behavior are related to the dynamics of the social
structures in which individuals are embedded.
This implies that in order to study social mechanisms such as social
selection or peer influence, we need to model the evolution of social networks
and the attributes of network actors as interdependent processes.
The stochastic actor-oriented model is a statistical approach to study
network-attribute coevolution based on longitudinal data.
In its standard specification, the coevolving actor attributes are assumed to
be measured on an ordinal categorical scale.
Continuous variables first need to be discretized to fit into such a modeling framework.
This article presents an extension of the stochastic actor-oriented model
that does away with this restriction by using a stochastic differential
equation to model the evolution of a continuous attribute.
We propose a measure for explained variance and give an interpretation
of parameter sizes. The proposed method is illustrated by a
study of the relationship between friendship, alcohol consumption,
and self-esteem among adolescents.
The corresponding extension of RSiena will be made available
later this year.
April 25 21, 2019: Publication of the paper about the
four/five-parameter model for effects of actor covariates on networks,
which earlier was at the ArXiV.
Abstract
We consider the specification of effects of numerical actor attributes
in statistical models for directed social networks.
A fundamental mechanism is homophily or assortativity, where
actors have a higher likelihood to be tied with others having similar
values of the variable under study. But there are other mechanisms that
may also play a role in how the attribute values of two actors influence
the likelihood of a tie. We discuss three additional mechanisms:
aspiration to send ties to others having high values; conformity
in the sense of sending more ties to others whose values are close to what
may be considered the `social norm';
and sociability, where those having higher values will tend to send
more ties generally. These mechanisms may operate jointly,
and then their effects will be confounded.
We present a specification representing these effects simultaneously
by a four-parameter quadratic function of the values of sender and receiver.
Greater flexibility can be obtained by a five-parameter extension.
We argue that empirical researchers often overlook the possibility
that homophily may be confounded with these other mechanisms,
and that for actor attributes that have important effects on directed networks,
these specifications may provide an improvement.
An illustration is given of the dependence of advice ties on
academic grades in a network of MBA students,
analyzed by the Stochastic Actor-oriented Model.
Associated with this paper is the
new script SelectionTables.r
which is available from the
scripts page.
February 26, 2019:
Main changes in RSiena and RSienaTest visible to users are the
following.
January 28, 2019:
For analyses using RSiena with an amount of missing data that is
more than very small, this is a definite improvement over the default method
used in RSiena!
December 27, 2018:
Christoph Stadtfeld, András Vörös, Timon Elmer, Zsófia Boda,
and Isabel J. Raabe (2018).
Integration in emerging social networks explains academic failure and success.
Proceedings of the National Academy of Sciences, in press.
Abstract (partial). December 12, 2018:
jimi Adams and David Schaefer (2018).
Visualizing Stochastic Actor-based Model Microsteps.
Socius, "Sociological Research for a Dynamic World", Sage Publications. December 6, 2018:
Main changes in RSiena and RSienaTest visible to users are the
following.
October 30, 2018:
There was an error in sienaGroupCreate for non-centered actor covariates.
This now is corrected in version 1.2-13.
Main changes in RSiena and RSienaTest visible to users are the
following.
September, 2018: review of studies using actor-oriented models
for studying peer influence on anti-social behaviors.
Abstract (second half only)
Already on the web in 2016: practical recommendations for data collection
for longitudinal network studies in school settings.
Abstract
May 31, 2018: meta-analysis of results of actor-oriented models.
Owen Gallupe,John McLevey, and Sarah Brown (2018).
Selection and Influence: A Meta-Analysis of the Association
Between Peer and Personal Offending. Abstract
May 13, 2018: the new official version 1.2-12 of RSiena
is available from
CRAN.
Except for a detail on a help page, it is identical to version 1.2-11.
May 6, 2018: new version 1.2-11 of RSiena and RSienaTest
available from the
downloads page
and
the RSiena project at R-Forge.
Main changes in RSiena and RSienaTest visible to users are the
following. For the definition of the effects, consult the manual.
May 5, 2018: paper
'Statistical Power in Longitudinal Network Studies' by Christoph Stadtfeld, Tom A. B. Snijders,
Christian Steglich, and Marijtje van Duijn.
Sociological Methods and Research, 2018.
Abstract
Longitudinal social network studies can easily suffer from insufficient statistical
power. Studies that simultaneously investigate change of network ties
and change of nodal attributes (selection and influence studies) are particularly
at risk because the number of nodal observations is typically much
lower than the number of observed tie variables. This article presents a
simulation-based procedure to evaluate statistical power of longitudinal
social network studies in which stochastic actor-oriented models are to be
applied. Two detailed case studies illustrate how statistical power is strongly
affected by network size, number of data collection waves, effect sizes,
missing data, and participant turnover. These issues should thus be explored
in the design phase of longitudinal social network studies.
March 24, 2018: new version 1.2-10 of RSiena and RSienaTest
available from the
downloads page
and
the RSiena project at R-Forge.
Versions 1.2-9 and 1.2-10 are almost identical.
March 21, 2018: Paper at ArXiV about model specification
for numerical actor covariates.
The paper later was published, see entry for April 25, 2019.
February 24, 2018: first publication using sienaBayes.
Abstract
This article introduces a novel theoretical approach for understanding racial fluidity,
emphasizing the social embeddedness of racial classifications.
We propose that social ties affect
racial perceptions through within-group micromechanisms, resulting in
discrepancies between racial
self-identifications and race as classified by others. We demonstrate this
empirically on data from 12
Hungarian high school classes with one minority group (the Roma) using
stochastic actor-oriented models for the analysis of social network panel data.
We find strong evidence for social influence:
individuals tend to accept their peers' judgement about another student's
racial category; opinions
of friends have a larger effect than those of nonfriends. Perceived social
position also matters: those
well-accepted among majority-race peers are likely to be classified as majority
students themselves.
We argue that similar analyses in other social contexts shall lead to a
better understanding of race
and interracial processes.
February 24, 2018:
error in distance-2 effects.
The current versions of RSiena and RSienaTest at R-Forge have errors
for the distance-2 selection effects such as altDist2 and altInDist2.
The current version of RSiena at the Siena
downloads page does not have this error.
February 16, 2018:
more help for selection and influence tables.
In the manual, the section about selection and influence tables was updated.
September 11, 2017: new version 1.2-4 of RSiena and RSienaTest
available from the
downloads page
and
the RSiena project at R-Forge.
Main changes in RSiena and RSienaTest visible to users:
September 9, 2017: error in version 1.2-3 of RSiena and
RSienaTest
The option include=FALSE does not work in functions setEffect
and includeEffects of RSiena and RSienaTest,
version 1.2-3. This has been repaired in R-Forge version 1.2-4. The version on
CRAN, however, still is 1.2-3 and has this error.
source("correction_excludeEffects.r")
and this should do the trick.
September 8, 2017: new versions 1.2-3 of RSiena at CRAN;
For the moment, the versions of RSiena at CRAN and R-Forge are identical.
The creation of the new CRAN version should have made the package more stable. September 4, 2017: new versions 1.2-1 of RSiena
and 1.2-2 of RSienaTest are available from the
downloads page
and
the RSiena project at R-Forge.
Short overview of main changes:
A more complete listing of all changes is as follows.
Main changes in RSiena and RSienaTest visible to users:
June, 2017: RSiena wins the
INSNA William D Richards Award.
This is a
"lifetime achievement award",
granted at the 2017 International Sunbelt Social Networks Conference (Beijing) to Tom Snijders and
Christian Steglich, for "publically available social network analysis software without which
it would be impossible to study social networks".
May 12, 2017: new versions 1.1-307 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at R-Forge.
Main changes in RSiena and RSienaTest visible to users:
Changes in RSienaTest:
March 21, 2017: Bug for maximum likelihood and sienaBayes estimation due to structural values
For some time it has been known that maximum likelihood estimation using siena07
may have problems with structural zeros;
also, it has been found that sienaBayes sometimes hangs after the initialization phase.
To solve such issues, the following remedies may be applied.
In version 1.1-306 of RSiena/Test, the functions siena07 and sienaBayes
include checks for these configurations,
so that they lead to decent error messages instead of R hanging indecently.
October 10, 2016: new versions 1.1-302 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at R-Forge.
Main changes in RSiena and RSienaTest visible to users:
August 18, 2016: new versions 1.1-296 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at R-Forge.
Note: for the Mac version of RSienaTest, use the version
at the downloads page
rather than at R-Forge.
Changes in RSiena and RSienaTest:
Incompatibilities
It is not guaranteed that objects created by earlier versions of
RSiena/Test can still be manipulated by more recent versions.
Sometimes they will, but there also may be clashes.
Therefore when starting to work with a new version of RSiena/Test
it is advisable to create data objects and effects objects anew.
If this leads to serious problems that cannot be solved
by updating your objects, you could bring this up in the
Siena/Stocnet yahoo discussion group.
Bugs
Minor bugs
In x$truncation * fra/maxRatio :
This can safely be ignored (it's just a warning, due to stricter and safer
rules in R 3.4.0). In the next version of RSiena/Test (1.1-306)
this warning will have disappeared.
For past bugs, see below.
May 28, 2016: new versions 1.1-294 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at R-Forge.
Changes in RSiena and RSienaTest:
Changes in RSiena:
Additional advice for how to achieve good convergence (in cases where
this is difficult, e.g., complicated models and data sets)
is given in Section 6.2 of the
manual.
February 4, 2016: new versions 1.1-290 of RSiena
and 1.1-291 of RSienaTest are available from
the RSiena project at R-Forge and from the
downloads page.
Changes in RSiena and RSienaTest:
Changes in RSienaTest:
This book is announced here.
It is mostly about cross-sectional network data.
In Chapter 2, The Multiple Flavours of Multilevel Issues for Networks
by Tom Snijders, there is a section about the
Stochastic Actor-Oriented Models for Multilevel Networks.
For modeling transitivity in actor-oriented models,
the gwesp effect often is better than the 'traditional'
transTrip and transTies effects.
'Better' in the sense that often it will give a better fit
(or, as good a fit as using the other two together) and better convergence.
The gwesp effects (there are several: gwespFF, gwespBB, etc.)
are extensively explained in the manual.
gwespFF is similar to transTrip, and gwespBB is similar to cycle3.
Then there are gwespFB, gwespBF, and gwespRR, which are other ways to
improve the fit of the triad census in cases where this is desirable.
It probably is usually not a big difference, but one might use
gwespFF as a default instead of transTrip or transTies, unless one
does not want to go through the hassle of the extra explanation.
Note that the gwesps are elementary effects. The
difference between elementary and evaluation effects also is explained in the manual.
A practical consequence is that they can be used in interactions
(their interactionType is dyadic). Therefore, the gwesp-related effect that has
the similar role as transRecTrip
(cf. Per Block, 2015, "Reciprocity, transitivity, and the mysterious three-cycle",
Social Networks, 40:163-173) is the interaction between gwespFF and reciprocity,
and can be defined by includeInteraction().
Since version 1.1-290, the internal effect parameter of such an interaction
can be set straightforwardly by includeInteraction().
See the help page for this function (and possible also the help page for
setEffect()).
September 10, 2015: new version (1.1-289) of RSiena
and RSienaTest is available from
the RSiena project at R-Forge and from the
downloads page.
Changes in RSiena and RSienaTest:
July 21, 2015: new version (1.1-288) of RSiena
and RSienaTest is available from
the RSiena project at R-Forge and from the
downloads page.
Changes in RSiena and RSienaTest:
Some recent publications (many others are at the
applications page)
A paper that presents a method for meta-analysis, which is
(presumably) better than siena08().
It uses R package mvmeta; note that
another package for meta analysis that may be used is metafor.
These packages can include explanatory variables at the
network level.
Also see the RSiena manual, Section 11.2:
Abstract
This paper introduce new methods for multilevel meta network analysis.
The new methods can combine results from multiple network models,
assess the effects of predictors at network or higher levels
and account for both within- and cross-network correlations
of the parameters in the network models.
They are demonstrated by applying them to
network dynamics of a smoking prevention intervention
implemented in 76 classes of six middle schools in China.
Remark: on p. 48 of this paper, it is stated that
'Previous meta network analysis, maybe except the Fisher's
method for combining independent p-values (Snijders and Bosker, 2012;
Ripley et al., 2014), mostly assumes that the estimated parameters
for a particular variable in the network models are generated by a
common effect'.
This is incorrect.
In the method of Snijders and Baerveldt (2003) implemented in RSiena
function siena08(),
the model is a random effects model (following Cochran, 1954),
but without the assumption of a normal distribution.
The statement is correct that Fisher's method does not
make the assumption of a common effect.
Abstract
This paper examines the interplay between friendship (best friend)
and antipathy (dislike) relationships among adolescents.
Based on (structural) balance theory, it was expected that
friendships would be formedor maintained when two adolescents
disliked the same person (shared enemy hypothesis), that friends
would tend to agree on whom they disliked (friends' agreement hypothesis),
that adolescents would tend to dislike the friends of those
they disliked (reinforced animosity hypothesis), and
that they would become or stay friends with dislikes of
dislikes (enemy's enemy hypothesis). Support was found
for the first three hypotheses, and partially for the fourth.
Abstract
The (co)evolution of bullying/victimization
and defending was examined
within three elementary schools across three
time points within a year. Most bullies and defenders were in
the same grade as the victims, although a substantial number
of bullies and defenders were in other grades
(most often one grade higher).
In line with goal-framing theory, multiplex network analyses
provided evidence for the social support hypothesis
(victims with the same bullies defended each other over time)
as well as the retaliation hypothesis (defenders run the risk
of becoming victimized by the bullies of the victims they defend).
In addition, the analysis revealed that bullies with the same
victims defended each other over time and that defenders of
bullies initiated harassment of those bullies' victims.
Abstract
This paper analyses how concurrent multilevel
processes of (internal) organizational and (external) network
change affect one another over time.
New effects are presented that afford specification and identification
of two apparently conflicting micro-relational mechanisms
that jointly affect decisions to modify the portfolio of
internal organizational activities. The first mechanism,
assimilation, makes network partners more similar by facilitating
the replication and diffusion of experience. The second mechanism,
functional differentiation, operates to maintain and amplify differences
between network partners by preventing or limiting internal
organizational change. Data are analyzed that were collected on a
regional community of hospital organizations connected by
collaborative patient transfer relations observed over a
period of seven years. It is found that processes of social
influence conveyed by network ties may lead both to
similarity and differences among connected organizations.
Abstract
Partnership ties shape friendship networks through
different social forces. First, partnership ties
drive clustering in friendship networks:
individuals who are in a partnership tend to have
common friends and befriend other couples.
Second, partnership ties influence the level of homophily
in these emerging friendship clusters.
Partners tend to be similar in a number of attributes (homogamy).
If one partner selects friends based on preferences for homophily,
then the other partner may befriend the same person regardless
of whether they also have homophilic preferences.
Thus, two homophilic ties emerge based on a single partner's
preferences. This amplification of homophily can be observed
in many attributes (e.g., ethnicity, religion, age).
Gender homophily, however, may be de-amplified,
as the gender of partners differs in heterosexual partnerships.
In our study, we follow dynamic friendship formation among
126 individuals and their cohabiting partners in a
university-related graduate housing community over
a period of nine months.
June/July, 2015: Advanced Siena users' workshop slides of Sunbelt 2015
At the literature tab of the
Siena pages, section Presentations (teaching material),
there are available the slides of the
Advanced Siena users' workshops of Sunbelt 2014 and 2015.
May 22, 2015: Stronger convergence criterion necessary
It turns out that the usual convergence criterion, tmax <= 0.10 where tmax
is the maximum absolute value of the t-ratios for convergence,
is not a satisfactory measure of convergence of the Robbins-Monro estimation
algorithm used for RSiena.
Earlier, simulation results had been obtained that provided support
for this criterion, but it now has been found that especially when
using non-centered covariates this criterion may be inadequate; and
if models with non-centered covariates lead to potential problems
for this convergence criterion,
then it cannot be excluded that other models also do.
Thus, the new rule for convergence is that
tmax <= 0.10 (as before) and tconv.max <= 0.25 (new); the latter condition is the
more important one. This now is mentioned in the RSiena manual.
The conclusion is that RSiena users are advised to reconsider their estimation
results and continue the estimation until this criterion is satisfied.
There is a possibility of differences in the results;
it is not expected that there will be many important differences,
but the possibility does exist.
May 22, 2015: new version (1.1-286) of RSiena
and RSienaTest is available from
the RSiena project at R-Forge.
Changes in RSiena and RSienaTest:
Some past bugs
Slides of presentations in 2014 on 'Advanced Siena' issues
Some recent publications (many others are at the
applications page)
First a position paper about coevolution that contains no application
of Siena, but mentions it as a tool.
Abstract
Then some applications that may be of general interest.
Abstract
Abstract
Abstract
Abstract April 2, 2015: new version (1.1-284) of RSiena
and RSienaTest is available from
the RSiena project at R-Forge.
Changes in RSiena and RSienaTest:
Changes in RSienaTest:
December 12, 2014: new version (1.1-282) of RSiena
and RSienaTest is available from
the RSiena project at R-Forge.
Changes in RSiena and RSienaTest:
Changes in RSienaTest:
November 19, 2014: new version (1.1-280) of RSienaTest
is available from
the RSiena project at R-Forge.
Version 1.1-280 is almost the same as 1.1-279.
Changes in RSienaTest:
November 13, 2014: new version (1.1-279) of RSiena
is available from
the RSiena project at R-Forge,
and of RSienaTest from the
downloads page.
The main changes of version 1.1-279 compared to 1.1-278 are
the following.
There are two changes in RSiena and RSienaTest that will lead to
incompatibilities
with existing scripts, so that you will need to modify these.
All scripts at the website were modified accordingly.
Note, however,
that sensible error messages will be produced (hopefully) when
scripts according to the old implementations are used:
July 25, 2014: new version (1.1-278) of RSiena and RSienaTest
is available from the
downloads page.
The main changes of version 1.1-278 compared to 1.1-276 are
the following.
In general you can see the changes of the two packages
with respect to earlier versions in appendix B of
the Siena users' manual
and, more in detail, in the changeLog file in the source code.
July 16, 2014: sienaBayes
The function sienaBayes() in package RSienaTest version 1.1-276,
developed by Johan Koskinen and Tom Snijders, now has developed far enough
to be used.
This is a function for multilevel Siena analyses, i.e., for
analyzing network dynamics in multiple independent groups in
which the same data structure, number of waves, and model specification
hold, but (contrary to the existing multiGroup option)
with parameters that may differ between groups.
The R help page provides, as usual, the primary information.
Section 11.3 of the manual is further important documentation.
June 3, 2014: new version (1.1-276) of RSiena and RSienaTest
is available from
the
downloads page.
The main changes of version 1.1-276 compared to 1.1-274 are
the following.
April 15, 2014: Paper by Charlotte Greenan about diffusion of innovations
The paper by Charlotte Greenan,
Diffusion of innovations in dynamic networks, appeared on the website
of
Journal of the Royal Statistical Society: Series A (Statistics in Society).
Abstract
Further information
For an application, see
April 26, 2014: new version (1.1-274) of RSiena and RSienaTest
is available from R-Forge.
A new version (1.1-274) of RSiena and RSienaTest can
be obtained from
the RSiena project at R-Forge.
The main changes of version 1.1-274 compared to 1.1-254 are
the following.
February 17, 2014: new version (1.1-254) of RSiena and RSienaTest
is available from R-Forge.
A new version (1.1-254) of RSiena and RSienaTest can
be obtained from
the RSiena project at R-Forge.
The main changes of version 1.1-254 compared to 1.1-250 are
the following.
It should be noted that two changes were made that potentially
have an influence on some results obtained.
First, the effects gwespFF, gwespBB, gwespFB, gwespBF, gwespRR
were modified (thanks to Nynke Niezink)
to bring their parametrization in accordance with the literature.
This means that the 'old' parameter alpha' was effectively replaced by
alpha = -log(1-exp(alpha')); here alpha is the internal
effect parameter divided by 100. For the default alpha = log(2)
we have alpha' = alpha, so this means no difference.
December 4, 2013: new version (1.1-250) of RSiena and RSienaTest
available from R-Forge.
A new version (1.1-250) of RSiena and RSienaTest can be obtained from
the RSiena project at R-Forge.
The main changes of version 1.1-250 compared to 1.1-246 are:
October 31, 2013: new version (1.1-246) of RSiena and RSienaTest
available from R-Forge.
A new version (1.1-246) of RSiena and RSienaTest can be obtained from
the RSiena project at R-Forge.
The main changes of version 1.1-246 compared to 1.1-245 are:
October 17, 2013: new version (1.1-245) of RSiena and RSienaTest
available from R-Forge.
A new version (1.1-245) of RSiena and RSienaTest can be obtained from
the RSiena project at R-Forge.
The main changes of version 1.1-245 compared to 1.1-243 are
(for RSienaTest only, RSiena still is the same):
September 17, 2013: new version (1.1-243) of RSiena and RSienaTest
available from R-Forge.
A new version (1.1-243) of RSiena and RSienaTest can be obtained from
the RSiena project at R-Forge.
The main changes of version 1.1-243 compared to 1.1-241 are:
RSiena and RSienaTest are almost the same, except for function
sienaBayes(), which is still experimental and undocumented, and included only
in RSienaTest.
August, 2013: Special issue of
Journal of Research on Adolescence
on Network and Behavior Dynamics in Adolescence.
A
special issue of Journal of Research on Adolescence was published
about Network and Behavior Dynamics in Adolescence, almost
all papers using RSiena.
The editors are René Veenstra, Jan Kornelis Dijkstra,
Christian Steglich, and Maarten H. W. Van Zalk.
August 24, 2013: new version (1.1-241) of RSiena and RSienaTest
available from R-Forge.
A new version (1.1-241) of RSiena and RSienaTest can be obtained from
the RSiena project at R-Forge.
The main changes of version 1.1-241 compared to 1.1-232 are:
RSiena and RSienaTest are almost the same, except for function
sienaBayes(), which is still experimental and undocumented, and included only
in RSienaTest.
June 15, 2013: new version (1.1-231) of RSiena and RSienaTest available from
R-Forge
Bugs in sienaGOF repaired.
May 14, 2013: new version (1.1-230) of RSienaTest
Error in MaxDegree solved; sienaGOF() still contains a bug for structural zeros.
Version 1.1-230 repairs an error in the operation of MaxDegree
(set in sienaAlgorithmCreate / sienaModelCreate, used in siena07).
This error existed for some time, and it is unknown for how long
this was the case. Nynke Niezink repaired it (thank you!).
Everybody who has used the MaxDegree option is advised to
rerun the models that used this option, because the outcomes are
likely to be wrong. We apologize...
April 2013: new version (1.1-227) of RSienaTest
Version 1.1-227 of RSienaTest has been committed to
R-Forge.
A lot of functions and options that were up to now available in RSiena
have been ported to RSienaTest.
There is now (as from May 2) a new version of the script
sienaGOF_new.R
that demonstrates the basic functionality of sienaGOF().
More is to follow.
RSiena: Siena in R
Appendix B of the RSiena manual contains a list of changes compared to earlier versions.
The RSiena users' manual is frequently updated.
Likewise, the Siena scripts page
is frequently updated with new or improved scripts. The front page of the manual and
the header of the scripts report the date of publication.
Recently added features in RSiena are the possibility to estimate dynamics of multiple
interdependent networks, which may be one-mode or two-mode networks;
and the possibility to estimate models for networks with valued ties.
See the Siena scripts page
for examples.
For multiple networks, the elaboration and explanation are presented in:
Some recent publications (many others are at the
applications page)
Abstract
Note
Introduction:
Abstract
A newer version was uploaded on October 4, allowing control over
the texts written in the plots.
(in particular to the fact that
keyword parameter was dropped for function includeInteraction
in version 1.3.20).
To use it, you must use an effects object created by this (or a future) version!
Journal of the Royal Statistical Society, Series A, 186, 376–400.
DOI: https://doi.org/10.1093/jrsssa/qnac009
an incorrect value was given for the p-value of test statistic T2
for the null hypothesis that all parameters are 0.
This error is corrected in version 1.3.15.
The new version of
Network Science, First View, 1-22.
DOI:
https://doi.org/10.1017/nws.2022.6
This is to illustrate how to obtain the numerical description of the
distinction between selection and influence by a decomposition
of the Moran (or other) statistic according to Steglich,
Snijders & Pearson (2010, Sociological Methodology).
Now also including a section on the use of sienaGOF
for multi-group data sets created by sienaGroupCreate.
internal effect parameter added to diffusion rate effects
(avExposure etc.), to allow testing the hypothesis (Damon Centola)
that social influence occurs
when at least p network members already adopted the innovation
(e.g., p=2);
outOutAvIntn, as a two-mode analogue of avAlt;
outOutActIntn made available for more network types.
This implies that, for estimation by ML or
You can still use structural zeros as usual for MoM estimation;
also, structural zeros at the start of a wave can still be used to indicate
that the actor did not take part in the network during this period
(or, if it is for one tie variable only, that the tie is absent
during the entire period).
The method is described in
A script demonstrating its use is
GMoMscript.R.
Journal de la Société Française de Statistique,
156, 140-165.
Psychometrika, 84, 1068-1096.
DOI:
https://doi.org/10.1007/s11336-019-09676-3
Error notification.
This was corrected in version 1.2.28 of RSienaTest.
By the way, you can figure out the correct labels yourself:
the posterior means are produced for the parameters that are specified
as random effects, and the order is the same as it is in
the print of z itself.
Also, (presumably) they are correct for objects z directly created by
sienaBayes without subsequent intervention of glueBayes.
This replaces the earlier Yahoo group, which was in place since 2005.
Main changes in RSiena :
Main changes in RSienaTest :
Main changes in RSienaTest:
DOI:
https://doi.org/10.1177/2059799119884282.
Main changes in RSienaTest:
Sociological Methodology, in press.
DOI:
http://dx.doi.org/10.1177/0081175019842263
totally skip initial multigroup
estimation if prevAns is given with ((prevAns$n3 >= 500) & (!usePrevOnly)).
and its check for prior rates relaxed (to allow priorRatesFromData=2 leading to slightly different prior rates.)
There now is a
script at the
Siena scripts page
showing how to implement multiple imputation of missing data for
network panel data sets, as proposed in the paper
Robert W. Krause, Mark Huisman, and Tom A.B. Snijders (2018),
Multiple imputation for longitudinal network data.
Italian Journal of Applied Statistics, 30, 33-57.
http://sa-ijas.stat.unipd.it/sites/sa-ijas.stat.unipd.it/files/10.26398-IJAS.0030-002.pdf
DOI:
https://doi.org/10.1073/pnas.1811388115
Academic success of students has been explained with a variety of individual
and socioeconomic factors. Social networks that informally emerge within student
communities can have an additional effect on their achievement. We investigate
how social networks emerge between previously unacquainted students and how
integration in these networks explains academic success.
Our study measures multiple important dimensions of social ties between students:
their positive interactions, friendships, and studying relations.
By using statistical models for dynamic network data, we are able to
investigate the processes of social network formation in the cohort.
We find that friendship ties informally evolve into studying relationships
over the academic year. Studying together with others, in turn, has a strong
impact on students' success at the examination.
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
actor-based model.
See the
script for executing this at the Siena scripts page.
DOI:
https://doi.org/10.1016/j.dr.2018.08.002
The current
article presents a review of recent empirical studies that have used dynamic social network
analyses to study peer influence effects for different forms of antisocial
behavior (i.e., aggression, delinquency, externalizing behavior, weapon carrying)
as these forms may be differently affected
by peer influence. Studies that lump different kinds of antisocial behavior together as 'externalizing
behavior' show mixed results with regard to peer influence. With regard to the development
of delinquency and weapon carrying, peer influences were observed in studies that
had six month to one-year measurement intervals, but not in those with shorter intervals. With
regard to direct forms of aggression, peer influence was only observed in certain contexts and
depended on individual antisocial traits.
What is recommended for further advance in the field of
peer influence is to avoid container variables of antisocial behavior (such as 'externalizing behavior'),
to pay close attention to the role of status and belonging needs, and to focus more
strongly on a detailed examination of the sequential order of peer selection and influence processes
and their moderation by individual and contextual conditions.
http://www.mzes.uni-mannheim.de/publications/wp/wp-166.pdf
There is an increasing amount of literature on how to analyze
longitudinal data of complete social networks.
Guidance on how to collect such data in schools, however,
is both scant and desperately needed,
because longitudinal social network analysis has high data
requirements. Aiming to provide guidance for
future data collection of school-based networks, we share our experiences
gained in three different projects collecting longitudinal social
network data in different samples of German schools. This includes both
one large-scale study that relied on a nationally representative sample
of schools and two smaller studies
that targeted more specific geographical areas.
We discuss key decisions researchers have to make before data
collection, the definition of school samples, and the selection of schools.
We further offer advice on how to improve school participation and students'
response, describe the fieldwork, and
make recommendations for projects aiming to collect longitudinal social network data.
Journal of Quantitative Criminology, in press.
DOI:
https://doi.org/10.1007/s10940-018-9384-y.
Whether people are affected by the criminal behavior of peers
(the "influence" perspective) or simply prefer to associate with others
who are similar in their offending (the "selection" perspective) is a
long-standing criminological debate.
The relatively recent development of stochastic actor-oriented models
(SAOMs-also called SIENA models) for longitudinal social network
data has allowed for the examination of selection and influence effects
in more comprehensive ways than was previously possible.
This article reports the results of a systematic review and meta-analysis of
studies that use SAOMs to test for peer selection and influence effects.
Thanks to efforts by
Felix Schönenberger, it is now available on all R platforms, including Solaris.
extract.sienaBayes:
corrected error that occurred if called with
extracted="all" but there are no varying,
or no non-varying parameters.
DOI:
https://doi.org/10.1177/0049124118769113.
Main changes in RSiena and RSienaTest visible to users are the
following. For the effects, consult the manual.
Changes in RSienaTest:
In print.sienaBayesFit, include fixed parameters and
give credibility intervals for rate parameters; include variance parameters;
allow shorter ThinParameters;
print objects returned through partialBayesResult.RData.
Web:
https://arxiv.org/abs/1803.07172.
DOI:
http://dx.doi.org/10.15195/v5.a3.
If you wonder about whether the version you are using has this error,
the answer will be clear, because estimation with these effects will
run into errors and not finish seemingly adequately.
New scripts SelectionTables.r and
InfluenceTables.r
are available from the
scripts page.
You can work around this by not using this option, i.e., just creating an effects
object and including effects, not excluding them.
This works unless you wish to exclude effects
that are included by default, such as recip for directed and transTriads
for non-directed networks. Another way to work around this is by finding out
the row numbers of the effects you wish to exclude in the total effects object
(for which you can employ something like [print(myeff,includeOnly=FALSE)]),
and setting the include column in those rows to FALSE.
Still another way is to download the two files given here
(in most browsers you can download by right-clicking and then saving them on your computer;
do keep the names intact; Internet Explorer is lousy and tends to mess them up):
new version 1.2-3 of RSienaTest is available from the
downloads page
and
the RSiena project at R-Forge.
The only change in functionality between versions 1.2-3 and 1.2-1 is that the
totAlt effect now has been made available also for non-directed networks.
Changes in RSiena :
Changes in RSienaTest:
note that these are used by
sienaTimeTest and sienaGOF, so running those functions will be impossible
for sienaFit object obtained with lessMem=TRUE.
warning for impossible changes, see Section 11.3.1 of the manual;
if priorRatesFromData=2, change to different robust covariance matrix
estimator when this is necessary (i.e., for small number of groups);
in print.summary, also report nImproveMH;
a few lines added to help file.
This is a bug for versions up to and including 1.1-305.
This seems to be because likelihood-based estimation by RSiena
does not allow the following data configurations:
If data sets with such configurations are given to RSiena,
it will hang at the moment that it tries to create the chain for likelihood simulations.
This solution for siena07 is perfect because it does not change the data.
For sienaBayes it is less than perfect, but acceptable
if the fraction of such changes is small.
The solution for sienaBayes makes sense because, if the observations for dyad (i,j)
are a
structural zero for Wave 1 and a tie for Wave 2, then the tie must have
started some time before Wave 2; so the structural zero cannot have
persisted all the way from Wave 1 right up to Wave 2.
An example script to make such replacements is
changeForbiddenChanges.R
In the manual,
a section about sienaRI was added (Section 13.5).
Incompatibilities of functions that may lead to the necessity
to adapt scripts are listed below.
This will be corrected in the next version (1.1-306).
Recycling array of length 1 in vector-array arithmetic is deprecated.
Use c() or as.vector() instead.
The solution is simple: order the networks in sienaDataCreate so that
the one-mode networks come first, followed by the two-mode networks,
followed by the behavioral variables.
On the other hand, for goodness of fit testing by sienaGOF,
data with a "compositionChange" object are not treated properly;
there the structural zeros are necessary for treating changing
composition. But if you use NA codes for the tie variables of
absent actors, and these are indicated as such by a "compositionChange" object,
then sienaGOF will work properly.
Changes in RSienaTest:
February 22, 2016. Convergence issues; and AdSUM-2016
This - and more - was discussed at the third
Advanced Siena Users' Meeting (AdSUM-2016)
held in Zürich February 19-20, of which the slides are
available here.
January 19, 2016. New book: Multilevel Network Analysis
for the Social Sciences
Note that almost at the same time, a special issue appeared of the journal
Social Networks about the same theme.
September 12, 2015. Specification of transitivity for actor-oriented models
Changes in RSiena:
Note: this will influence convergence properties
(it should improve convergence).
Changes in RSienaTest:
Changes in RSiena:
Changes in RSienaTest:
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.03.006.
The fixed effects meta-analysis model is available in RSiena
as the multi-group option.
In siena08() also a ML estimate is implemented under
normality; see the manual.
However, the REML method used in An's paper, and available in
mvmeta and metafor, is usually slightly better
than the ML method; again, see the manual.
Two papers about co-evolution of two networks,
one positive and one negative:
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.05.003.
DOI:
http://dx.doi.org/10.1017/S0954579414000297
Co-evolution of a one-mode and a two-mode network,
with differentiation between creation and maintenance of ties:
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.04.010.
Network dynamics with actors grouped in pairs:
DOI:
http://dx.doi.org/10.1093/sf/sov079.
A stronger convergence criterion has been found that
does give an adequate signal of convergence (the past shows that you never
can be sure, but to our current knowledge there are convincing signs that this is adequate).
This is the overall maximum convergence ratio, described since some
time in the RSiena manual, and given with sienaFit objects as
the element tconv.max. This 'overall maximum convergence ratio'
is defined as the maximum convergence ratio for linear combinations of
the estimation statistics, while tmax is the maximum absolute
convergence ratio for only the set of statistics (i.e., coordinates)
directly used for the estimation. Section 3.2 of
Siena_algorithms.pdf
gives definitions and further explanations, and arguments for the use of the
criterion tconv.max <= 0.25.
Some examples were found, for models including non-centered covariates,
with important differences between results for estimations where only
tmax <= 0.10, and estimations where also tconv.max <= 0.25.
This shows that the earlier convergence criterion is not reliable. (@*&#!!)
The stronger criterion can be satisfied by repeating, if necessary,
estimation with successive use of the prevAns parameter, as before,
and as explained in the manual. To improve convergence, some additional
options for siena07() were developed. These are given as parameters to
sienaAlgorithmCreate(). The main parameters for this purpose are
doubleAveraging, diagonalize (this option was corrected and improved),
and n2start. Their use is explained in Section 6.1.3 of the
new edition of the
RSiena users' manual.
If one has a reasonable way of imputing the missing values, this
will give more adequate simulations than the imputation of everything
by the global mean.
By the way, numerical overflow also will be a limitation for applying RSiena to very large
networks for some other effects.
This was corrected in version 1.1-284.
This was corrected in version 1.1-284.
This was corrected in version 1.1-284.
This only affected the se component, the standard errors given
in the usual output and print of siena07() were not affected.
DOI:
http://dx.doi.org/10.1177/0149206315573996.
This paper focuses on an emergent debate about the microfoundations
of organizational social networks.
We consider three theoretical positions: an individual agency
perspective suggesting that people, through their individual
characteristics and cognitions, shape networks;
a network patterning perspective suggesting that networks,
through their structural configuration, form people;
and a coevolution perspective suggesting that people,
in their idiosyncrasies, and networks, in their differentiated
structures, coevolve. We conclude that individual attitudes,
behaviors, and outcomes cannot be fully understood without
considering the structuring of organizational contexts in which
people are embedded, and that social network structuring and change
in organizations cannot be fully understood without considering the
psychology of purposive individuals. To guide future research,
we identify key questions from each of the three theoretical perspectives and,
particularly, encourage more research on how individual
actions and network structure coevolve in a dynamic process
of reciprocal influence.
DOI:
http://dx.doi.org/doi:10.1016/j.socnet.2014.10.005.
Reciprocity and transitivity are the two most important structural mechanisms
underlying friendshipnetwork evolution. While on their own they are understood
in great detail, the relation between them israrely studied systematically.
Are friendships outside of social groups more or less likely to be reciprocated
than friendships embedded in a group? Using a theoretical framework that
focusses on the situations inwhich friends interact and the social structures
that stabilise one-sided friendships, I propose that thetendency towards
reciprocation of friendships within transitive groups is usually lower
than outside of transitive groups.
In a meta-analysis of two datasets including 29 friendship networks using
stochastic actor-oriented models, the interaction between reciprocity
and transitivity is analysed. Supporting the theoretical reasoning,
the interaction is consistently negative. Second, the tendency against
forming three-cycles in friendship networks, which was consistently
found in previous studies, is shownto be spurious and a result of neglecting
to control for the tendency against reciprocation in transitive groups.
The tendency against three-cycles is commonly seen as an indicator that
unreciprocated friendships indicate local hierarchy differences between
individuals; this proposition has to be re-evaluated in light of the
findings of this study. Future studies that analyse the evolution of
friendship networks should consider modelling reciprocation in transitive
triplets and potentially omit modelling three-cycles.
DOI:
http://dx.doi.org/10.1017/nws.2014.17.
Homophily -the tendency for individuals to associate with similar others- is one of the
most persistent findings in social network analysis. Its importance is established along the
lines of a multitude of sociologically relevant dimensions, e.g. sex, ethnicity and social
class. Existing research, however, mostly focuses on one dimension at a time. But people are
inherently multidimensional, have many attributes and are members of multiple groups. In this
article, we explore such multidimensionality further in the context of network dynamics. Are
friendship ties increasingly likely to emerge and persist when individuals have an increasing
number of attributes in common? We analyze eleven friendship networks of adolescents,
draw on stochastic actor-oriented network models and focus on the interaction of established
homophily effects. Our results indicate that main effects for homophily on various dimensions
are positive. At the same time, the interaction of these homophily effects is negative. There
seems to be a diminishing effect for having more than one attribute in common. We conclude
that studies of homophily and friendship formation need to address such multidimensionality
further.
DOI:
http://dx.doi.org/10.1007/s11577-014-0271-8.
One way to think about social context is as a sample of alters. To understand
individual action, therefore, it matters greatly where these alters may be coming
from, and how they are connected. According to one vision, connections among
alters induce local dependencies-emergent rules of social interaction that generate
endogenously the observed network structure of social settings. Social selection
is the decision of interest in this perspective. According to a second vision, social
settings are collections of social foci-physical or symbolic locales where actors
meet. Because alters are more likely to be drawn from focused sets, shared social
foci are frequently considered as the main generators of network ties, and hence of
setting structure. Affiliation to social foci is the decision of central interest in this
second view. In this paper we show how stochastic actor-oriented models (SAOMs)
originally derived for studying the dynamics of multiple networks may be adopted
to represent and examine these interconnected systems of decisions (selection and
affiliation) within a unified analytical framework. We illustrate the empirical value
of the model in the context of a longitudinal sample of adolescent participating in
the Glasgow Teenage Friends and Lifestyle Study. Social selection decisions are
examined in the context of networks of friendship relations. The analysis treats
musical genres as the main social foci of interest.
DOI:
http://dx.doi.org/10.1177/0022146514532817.
This study investigates whether peer influence on smoking among
adolescents is asymmetrical. We hypothesize that several
features of smoking lead peers to have a stronger effect on
smoking initiation than cessation. Using data from the National
Longitudinal Study of Adolescent Health we estimate a
dynamic network model that includes separate effects for
increases versus decreases in smoking, while also controlling
for endogenous network change. We find that the impact of peer
influence is stronger for the initiation of smoking than smoking cessation.
Adolescents rarely initiate smoking without peer influence but
will cease smoking while their friends continue smoking.
We discuss the implications of these results for theories of
peer influence and health policy.
Changes in RSiena and RSienaTest:
Changes in RSienaTest:
It is still a beta version, and we are in a phase of collective collection of
information about how to use it.
The wiki-type of interactive website that was mentioned here previously
has been discontinued because it was too slow.
Please use the regular
Siena-Stocnet discussion list for interaction about sienaBayes.
The evolution of a dynamic social network and the diffusion
of an innovation are jointly modelled, dependent on one another,
by using an extension of a stochastic actor-oriented model developed
by Snijders, which is modified so that the adoption times follow a
proportional hazards model. The asymptotic behaviour of the
method-of-moments estimator is examined.
The model is demonstrated on a data set involving the initiation
of cannabis smoking among adolescents, and a simulation study is presented.
This paper models the co-evolution of networks and behavior for the case
where the behavior is a binary variable that can only increase.
The basic idea is that such a variable indicates a time to event.
This is also called adoption of innovation, event history analysis,
duration analysis, survival analysis, etc.
The special aspect of this model is that effects are contained mainly
in the rate function instead of the evaluation function. This yields
the proportional hazards model for the time-to-event distribution.
The implemented effects are described in the
RSiena manual
in Section 12.2.4: search for the average exposure effect
(`avExposure') which is the first of these effects mentioned.
DOI:
http://dx.doi.org/10.1111/jora.12064.
A version for Mac is available from the
downloads page.
Indlekofer, Natalie, and Brandes, Ulrik, (2013).
Relative importance of effects in stochastic actor-oriented models.
Network Science, Vol. 1, Issue 3, 278-304.
DOI:
http://dx.doi.org/10.1017/nws.2013.21.
Second, in the help page for sienaGOF-auxiliary, geodesic distances
were changed to non-directed. This makes more sense usually and was done to
avoid runtime errors that occurred very rarely.
Note that R also was updated a few weeks ago to R 3.0.0,
so it makes sense to first
update R and then
RSienaTest.
The main further changes are:
Tom A.B. Snijders, Alessandro Lomi, and Vanina Jasmine Torló (2013).
A model for the multiplex dynamics
of two-mode and one-mode networks,
with an application to employment preference, friendship, and advice.
Social Networks, 35, 265-276.
DOI: http://dx.doi.org/10.1016/j.socnet.2012.05.005.
For two-mode networks, the article mentioned above gives some explanation, but
a more specific explanation and example are given in:
Important new options in RSiena since April 2010
are the sienaGOF and sienaTimeTest functions contributed by Josh Lospinoso,
respectively for goodness of fit checking and
for testing time homogeneity for data with 3 or more waves.
See the manual for further explanation of these functions.
DOI: http://dx.doi.org/10.1177/1094428111430541.
p. 363-397 in Alessandro Lomi, J. Richard Harrison (ed.),
The Garbage Can Model of Organizational Choice: Looking Forward at Forty,
Research in the Sociology of Organizations, Volume 36, Emerald Group Publishing.
DOI:
http://dx.doi.org/10.1108/S0733-558X(2012)0000036017.
Further publications are listed at the
webpage with literature
and the
webpage with further applications.
DOI:
http://dx.doi.org/10.1016/j.ssresearch.2013.05.001.
Alcohol use is pervasive in adolescence. Though most research is
concerned with how friends influence drinking, alcohol is also
important for connecting teens to one another. Prior studies have
not distinguished between new friendship creation, and existing
friendship durability, however. We argue that accounting for
distinctions in creation-durability processes is critical for
understanding the selection mechanisms drawing drinkers into
homophilous friendships, and the social integration that results.
In order to address these issues, we applied stochastic actor
based models of network dynamics to National Longitudinal Study
of Adolescent Health data. Adolescents only modestly prefer new
friendships with others who drinker similarly, but greatly prefer
friends who indirectly connect them to homophilous drinkers.
These indirect homophilous drinker relationships are shorter lived,
however, and suggest that drinking is a social focus that connects
adolescents via proximity, rather than assortativity.
These findings suggest that drinking leads to more situational
and superficial social integration.
This paper differentiates between creation and maintenance (endowment effect;
called durability here) of ties and between homophily at
distances 1 and 2. For both, important results are found.
Network and Behavior Dynamics in Adolescence.
René Veenstra, Jan Kornelis Dijkstra, Christian Steglich
and Maarten H. W. Van Zalk (2013).
Network-Behavior Dynamics.
Journal of Research on Adolescence, 23, 399-412.
DOI:
http://dx.doi.org/10.1111/jora.12070.
DOI:
http://dx.doi.org/10.1111/j.1541-0072.2012.00457.x.
DOI:
http://dx.doi.org/10.1111/j.1541-0072.2012.00458.x.
DOI:
http://dx.doi.org/10.1146/annurev.soc.012809.102709.
DOI:
http://dx.doi.org/10.1073/pnas.1109739109.
Disentangling the effects of selection and influence is one of social
science's greatest unsolved puzzles: Do people befriend others
who are similar to them, or do they become more similar to their
friends over time? Recent advances in stochastic actor-based modeling,
combined with self-reported data on a popular online social
network site, allow us to address this question with a greater degree
of precision than has heretofore been possible. Using data on
the Facebook activity of a cohort of college students over 4 years,
we find that students who share certain tastes in music and in
movies, but not in books, are significantly likely to befriend one
another. Meanwhile, we find little evidence for the diffusion of
tastes among Facebook friends - except for tastes in classical/jazz
music. These findings shed light on the mechanisms responsible
for observed network homogeneity; provide a statistically rigorous
assessment of the coevolution of cultural tastes and social relationships;
and suggest important qualifications to our understanding of
both homophily and contagion as generic social processes.
DOI:
http://dx.doi.org/10.1073/pnas.1109739109.