  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.
