Statistical modeling of networks enables us to fully characterize the entire system as well as make predictions regarding link formation. Latent models address these tasks by incorporating non-conditional dependencies through random effects. A notable example is the Bayesian spatial process-based model, which is particularly useful for avoiding overfitting issues that may arise in latent distance space models. In this paper, we provide the computational implementation of the model and evaluate its goodness of fit and predictive performance using synthetic networks. The model demonstrates strong capabilities in replicating network statistics and estimating the corresponding latent surface. We also propose an alternative fitting approach using a case-control algorithm. Based on the estimated log-likelihood, the model exhibits good performance in terms of prediction as well as goodness of fit.
Sosa et al. (Sat,) studied this question.