2007/11/07 by Peter D. Hoff, Hoff, Peter D. · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.0711.1146
openalex publication_date 2007/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.