The latent position model is a popular approach for the statistical analysis of network data. A key aspect of this model is that it assigns nodes to random positions in a latent space, and the probability of an interaction between each pair of nodes is determined by their distance, allowing researchers to visualize nuanced structures via a latent embedding of the graph. Missing data is a...
Longitudinal social network data, observed as discrete-time snapshots, can be modeled using the dynamic stochastic block model, which assumes that nodes are clustered into unobservable groups and that block memberships evolve according to a hidden Markov chain. This model can be used as an early warning system by predicting the occurrence of future links through the evolution of latent block...
We propose a dynamic extension of the Mixture of Latent Trait Analyzers (MLTA) for a bipartite network. Specifically, we move along a Hidden Markov Model framework to account for the dynamic nature of the data and enable a dynamic clustering of sending nodes over time. A multidimensional continuous latent variable (trait) is assumed to account for residual, unobserved, time-constant, latent...
This paper offers methodological guidelines for the application of blockmodelling (BM), a clustering technique that historically informed heterodox analyses of trade but has since fallen out of favour, to the internataional trade network. It also puts these recommendations at work in a two-snapshot longitudinal case study into the the transformation of international trade under the first Trump...
