Multiplicative latent factor models for description and prediction of social networks

We discuss a statistical model of social network data derived from matrix representations and symmetry considerations. The model can include known predictor information in the form of a regression term, and can represent additional structure via sender-specific and receiverspecific latent factors. This approach allows for the graphical description of a social network via the latent factors of the nodes, and provides a framework for the prediction of missing links in network data. Some key words: eigenvalue decomposition, exchangeability, prediction, singular value decomposition, social network, visualization.