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p1 is now 30 years old, and research has progressed considerably over
two decades ... see for example the chapters on statistical
Carrington, et al. (2005).
p1, as well as p2, assumes dyadic independence, which is a rather
The new models, all based on the p* framework first proposed by Frank
in 1986, have opened up network research to realistic and
parametric structures. And we now have good estimation techniques
As Tom says here, p* (which is more accurately an exponential family
of random graphs, rather than
an "ERG" or an "ERGM") is a hot topic and one can do lots of cool
things with it
(if you are careful).
On Feb 8, 2007, at 4:53 AM, Tom Snijders wrote:
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> Hi readers,
> Christophe, the use of the p1 model for modeling the effects of
> node level independent variables on binary dyadic (relational)
> dependent variables is not such a good idea any more. Better than
> the p1 model is the p2 model, see
> Duijn, M.A.J. van, Snijders, T.A.B. & Zijlstra, B.J.H., (2004). P2:
> a random effects model with covariates for directed graphs.
> Statistica Neerlandica, 58, 234-254.
> which is part of the Stocnet suite, see http://stat.gamma.rug.nl/
> ; and the ERG model, see the papers-in-press of the special issue
> of Social Networks edited by Garry Robins and Martina Morris. The
> ERG model represents network structure such as triadic closure,
> while the p2 model is restricted to modeling differences in
> popularity and acitivity of nodes (like p1). Estimation for the
> ERGM is implemented in SIENA (again, in the Stocnet suite) and in
> Statnet (an R package).
> Best wishes,
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