Multiplicative latent factor models for description and prediction of social networks

Peter D. Hoff ()
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Peter D. Hoff: University of Washington

Computational and Mathematical Organization Theory, 2009, vol. 15, issue 4, No 2, 272 pages

Abstract: 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.

Keywords: Eigenvalue decomposition; Exchangeability; Prediction; Singular value decomposition; Social network; Visualization (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s10588-008-9040-4

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