  The Exponential-family Random Graph Model (ERGM) is a powerful model to fit
networks with complex structures. However, for dynamic valued networks whose
observations are matrices of counts that evolve over time, the development of
the ERGM framework is still in its infancy. To facilitate the modeling of dyad
value increment and decrement, a Partially Separable Temporal ERGM is proposed
for dynamic valued networks. The parameter learning algorithms inherit
state-of-the-art estimation techniques to approximate the maximum likelihood,
by drawing Markov chain Monte Carlo (MCMC) samples conditioning on the valued
network from the previous time step. The ability of the proposed model to
interpret network dynamics and forecast temporal trends is demonstrated with
real data.
