  Network diffusion models are applicable to many socioeconomic interactions,
yet network interaction is hard to observe or measure. Whenever the diffusion
process is unobserved, the number of possible realizations of the latent matrix
that captures agents' diffusion statuses grows exponentially with the size of
network. Due to interdependencies, the log likelihood function can not be
factorized in individual components. As a consequence, exact estimation of
latent diffusion models with more than one round of interaction is
computationally infeasible. In the present paper, I propose a trimming
estimator that enables me to establish and maximize an approximate log
likelihood function that almost exactly identifies the peak of the true log
likelihood function whenever no more than one third of eligible agents are
subject to trimming.
