Title: Exponential Random Graph Models with Big Networks: Maximum Pseudolikelihood Estimation and the Parametric Bootstrap,
Abstract: With the growth of interest in network data across fields, the Exponential
Random Graph Model (ERGM) has emerged as the leading approach to the
statistical analysis of network data. ERGM parameter estimation requires the
approximation of an intractable normalizing constant. Simulation methods
represent the state-of-the-art approach to approximating the normalizing
constant, leading to estimation by Monte Carlo maximum likelihood (MCMLE).
MCMLE is accurate when a large sample of networks is used to approximate the
normalizing constant. However, MCMLE is computationally expensive, and may be
prohibitively so if the size of the network is on the order of 1,000 nodes
(i.e., one million potential ties) or greater. When the network is large, one
option is maximum pseudolikelihood estimation (MPLE). The standard MPLE is
simple and fast, but generally underestimates standard errors. We show that a
resampling method---the parametric bootstrap---results in accurate coverage
probabilities for confidence intervals. We find that bootstrapped MPLE can be
run in 1/5th the time of MCMLE. We study the relative performance of MCMLE and
MPLE with simulation studies, and illustrate the two different approaches by
applying them to a network of bills introduced in the United State Senate.