title: An approximation method for improving dynamic network model fitting.
abstract: There has been a great deal of interest recently in the modeling and simulation of dynamic networks, i.e., networks that change over time. One promising model is the separable temporal exponential-family random graph model (ERGM) of Krivitsky and Handcock, which treats the formation and dissolution of ties in parallel at each time step as independent ERGMs. However, the computational cost of fitting these models can be substantial, particularly for large, sparse networks. Fitting cross-sectional models for observations of a network at a single point in time, while still a non-negligible computational burden, is much easier. This paper examines model fitting when the available data consist of independent measures of cross-sectional network structure and the duration of relationships under the assumption of stationarity. We introduce a simple approximation to the dynamic parameters for sparse networks with relationships of moderate or long duration and show that the approximation method works best in precisely those cases where parameter estimation is most likely to fail-networks with very little change at each time step. We consider a variety of cases: Bernoulli formation and dissolution of ties, independent-tie formation and Bernoulli dissolution, independent-tie formation and dissolution, and dependent-tie formation models.
authors: Harvard School of Public Health.
authors: Nicole Bohme
authors: NB
authors: Carnegie
authors: University of Wollongong.
authors: Pavel N
authors: PN
authors: Krivitsky
authors: Pennsylvania State University.
authors: David R
authors: DR
authors: Hunter
authors: University of Washington.
authors: Steven M
authors: SM
authors: Goodreau
keywords: Markov chain Monte Carlo; Separable temporal exponential random graph models (STERGMs); exponential random graph models (ERGMs); model fitting