I am pleased to announce the publication of the next paper in JoSS by Tom Snijders entitled "Markov Chain Monte Carlo Estimation of Exponential Random Graph Models". It is a powerful demonstration of the value of an online journal. I draw particular attention to his use of a JAVA applet in his paper, an applet that allows the user to explore the consequence of a set of parameter assumptions about a stochastic process in network formation. The parameters are familiar ones (density, node size, reciprocity, transitivity, node similarity, twostar). One cannot read this paper without trying the applet. Indeed, I look forward to seeing if other readers come to the same conclusion I did about the effect of transitivity on our models of networks (I will refrain from biasing anyone by announcing ahead of time what my conclusion was). To access the paper, go to: http://www2.heinz.cmu.edu/project/INSNA/joss/index1.html Abstract This paper is about estimating the parameters of the exponential random graph model, also known as the p_ model, using frequentist Markov chain Monte Carlo (MCMC) methods. The exponential random graph model is simulated using Gibbs or Metropolis- Hastings sampling. The estimation procedures considered are based on the Robbins-Monro algorithm for approximating a solution to the likelihood equation. A major problem with exponential random graph models resides in the fact that such models can have, for certain parameter values, bimodal (or multimodal) distributions for the su_cient statistics such as the number of ties. The bimodality of the exponential graph distribution for certain parameter values seems a severe limitation to its practical usefulness. The possibility of bi- or multimodality is reflected in the possibility that the outcome space is divided into two (or more) regions such that the more usual type of MCMC algorithms, updating only single relations, dyads, or triplets, have extremely long sojourn times within such regions, and a negligible probability to move from one region to another. In such situations, convergence to the target distribution is extremely slow. To be useful, MCMC algorithms must be able to make transitions from a given graph to a very different graph. It is proposed to include transitions to the graph complement as updating steps to improve the speed of convergence to the target distribution. Estimation procedures implementing these ideas work satisfactorily for some data sets and model specifications, but not for all. ------------------------------------------------- David Krackhardt, Professor of Organizations Academic homepage (address, etc.): http://www.heinz.cmu.edu/~krack/academic/ Journal of Social Structure: http://www.heinz.cmu.edu/project/INSNA/joss/