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 receiver-specific 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. © 2008 Springer Science+Business Media, LLC.

Duke Scholars

Author Peter David Hoff Statistical Science