Approximate Variational Estimation for a Model of Network Formation

We study an equilibrium model of sequential network formation with heterogeneous players. The payoffs depend on the number and composition of direct connections, but also the number of indirect links. We show that the network formation process is a potential game and in the long run the model converges to an exponential random graph (ERGM). Since standard simulation-based inference methods for ERGMs could have exponentially slow convergence, we propose an alternative deterministic method, based on a variational approximation of the likelihood. We compute bounds for the approximation error for a given network size and we prove that our variational method is asymptotically exact, extending results from the large deviations and graph limits literature to allow for covariates in the ERGM. A simple Monte Carlo shows that our deterministic method provides more robust estimates than standard simulation based inference.

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