  Dynamic networks are used in a variety of fields to represent the structure
and evolution of the relationships between entities. We present a model which
embeds longitudinal network data as trajectories in a latent Euclidean space. A
Markov chain Monte Carlo algorithm is proposed to estimate the model parameters
and latent positions of the actors in the network. The model yields meaningful
visualization of dynamic networks, giving the researcher insight into the
evolution and the structure, both local and global, of the network. The model
handles directed or undirected edges, easily handles missing edges, and lends
itself well to predicting future edges. Further, a novel approach is given to
detect and visualize an attracting influence between actors using only the edge
information. We use the case-control likelihood approximation to speed up the
estimation algorithm, modifying it slightly to account for missing data. We
apply the latent space model to data collected from a Dutch classroom, and a
cosponsorship network collected on members of the U.S. House of
Representatives, illustrating the usefulness of the model by making insights
into the networks.
