Latent factor models for relational data and social networks – Peter Hoff (University of Washington)

When:
February 6, 2007 all-day
2007-02-06T00:00:00-05:00
2007-02-07T00:00:00-05:00

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Abstract
Relational data consist of information that is specific to pairs (triples, etc) of objects. Examples include friendships among people, trade between countries, word counts in documents and interactions among proteins. A recent approach to modeling such data is via the use of latent factor models, in which the relationship between two objects is modeled as a function of some unobserved characteristics of the objects. Such a modeling approach is related to random effects modeling and to matrix decomposition techniques, such as the eigenvalue and singular value decompositions. In the context of several data analysis examples, I will describe and motivate this modeling approach, and show how latent factor models can be used for estimation, prediction and visualization for relational data.

Biography
Peter Hoff is an associate professor in the departments of Statistics and Biostatistics, and a member of the Center for Statistics and the Social Sciences at the University of Washington in Seattle.

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
3400 North Charles Street, Baltimore, MD 21218-2680

Center for Language and Speech Processing