Multiplicative latent factor models for description and prediction of social networks

Authors:
Peter D. Hoff
Affiliations:
Departments of Statistics, Biostatistics and the Center for Statistics and the Social Sciences, University of Washington, Seattle, USA 98195-4322
Venue:
Computational & Mathematical Organization Theory
Year:
2009

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Abstract

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.