Modeling homophily and stochastic equivalence in symmetric relational data

Details Modeling homophily and stochastic equivalence in symmetric relational data Modeling homophily and stochastic equivalence in symmetric relational data

2007-11-07

arxiv.org/abs/0711.1146v1

Statistics

Methodology

12 pages, 4 figures, 1 table

Scientific paper

This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-specific vectors of latent characteristics. This ``eigenmodel'' generalizes other popular latent variable models, such as latent class and distance models: It is shown mathematically that any latent class or distance model has a representation as an eigenmodel, but not vice-versa. The practical implications of this are examined in the context of three real datasets, for which the eigenmodel has as good or better out-of-sample predictive performance than the other two models.

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