Fitting Network Data Based on Latent Cluster Model

In the last ten years, social network analysis became a very popular topic in many different scientific fields, network models are also widely popular for representing the relationship of the network data. Network data exhibits transitivity and homophily of the actors. There exist many distance computation methods for the actors space distance, and two of them are the most famous for the latent position cluster model, here we used the latent cluster model which focus on clusters of actors or ties. In this paper, we compared two distance definition methods with different latent position cluster method, the two-stage method with Euclidean distance(TMED) model and the bayesian estimation method with Bilinear latent(BEBL) model. The model make simulate the network dataset easy, and compared the mcmc diagnostics.