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Dear Kim and social networkers,
p* and p2 do not contain each other either way. p* has an in principle
unlimited possibility for network effects (and p2 doesn't); the
practical limitation resides in the estimability of these effects (cf.
Snijders, Pattison, Robins, Handcock, New specifications for
exponential random graph models; To be published, Sociological
Methodology; downloadable from
p* now is often referred to as ergm (exponential random graph model).
On the other hand, p2 has random effects for representing between-actor
differences (and p* doesn't). However, in/out/mixed-twostars effects in
p* have to some degree a similar functionality; but the interpretability
of unexplained variances at the actor level, which is a feature of p2,
is not available in p*.
The focus of p2 is on testing effects of covariates, which can be
actor-bound or dyad-bound, and which can interact with reciprocity;
while controlling for differential actor tendencies to sending and
receiving ties, and for reciprocity. The focus of p*/ergm is on
structurally modeling networks (which may include covariate effects).
For p2, better estimation methods have been an advantage; recently there
have been advances in estimation methods for both p*/ergm and p2, and
p*/ergm now is in much better shape than with the earlier
pseudolikelihood methods. I think that with recent and almost finished
work on multivariate and multilevel p2, p2 retains an advantage
concerning availability of good estimation methods across a range of
model extensions. (The new January 2006 release of Stocnet will contain
a new p2 version with multivariate and multilevel options.) p*/ergm has
the advantage of a much better representation of structural network effects.
Kim Se Kwon wrote:
> ---------------------- Information from the mail header -----------------------
> Sender: Social Networks Discussion Forum <[log in to unmask]>
> Poster: Kim Se Kwon <[log in to unmask]>
> Subject: Does p* contain p2?
> ***** To join INSNA, visit http://www.insna.org *****
> I've read some papers about p* and one paper about p2.
> If I've read correctly, Equation 4 of reference 2 ;extends ;first p*
> (equation 1) to contain covariates. And p2 is 'the extension of p1 that
> doesn't assume dyadic independence and accounts for nodal & dyadic
> Does p* (equation 4 in reference 2) contain p2? Because p* has p1 as
> submodels, p2's model equation is very similar to p*'s. If not, what is the
> unique features of p2?
> Thanks in advance for any comments.
> --- Equations ---
> Equation 1 of Reference 2 ; : ; P(X = x) ;= exp(theta' * ;t(x)) / c(theta)
> Equation 4 of Reference 2 ; : ; P(X = x) = exp(vec(x)' Z beta + sum of
> alpha_k * d_k(x) + theta' * t(x)) / c(alpha, beta, theta) ; (d_k : the
> number of individuals with exactly k links, Z : exogenous dyadic
> --- References ---
> 1. Logit models and logistic regressions for social networks : I An
> Introduction to markov graphs and p*. Stanley ;Wasserman...
> 2. statnet: An R package for the Statistical Analysis and Simulation of
> Social Networks, Mark S. Handcock...
> 3. p2: a random effects model with covariates for directed graphs. Marijtje
> A. J. van Duijn...
> Best Regards
> Se Kwon, Kim
> KAIST (Korean Advanced Institute of Science & Technology)
> Mathematics Student
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Tom A.B. Snijders
Professor of Statistics and Methodology
Scientific Director, ICS
Department of Sociology, University of Groningen
Grote Rozenstraat 31
9712 TG Groningen
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SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.