Latent factor models for relational data and social networks
Peter Hoff, University of Washington
February 6, 2007
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.
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.