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.
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This research was supported by Office of Naval Research grant N00014-02-1-1011 and National Science Foundation grant SES-0417559.
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Hoff, P.D. Multiplicative latent factor models for description and prediction of social networks. Comput Math Organ Theory 15, 261–272 (2009). https://doi.org/10.1007/s10588-008-9040-4
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DOI: https://doi.org/10.1007/s10588-008-9040-4