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Leveraging relational autocorrelation with latent group models Neville, Jennifer Jensen, David Statistical relational learning probabilistic relational models latent variable models autocorrelation collective inference The presence of autocorrelation provides strong motivation for using relational techniques for learning and inference. Autocorrelation is a statistical dependency between the values of the same variable on related entities and is a nearly ubiquitous characteristic of relational data sets. Recent research has explored the use of collective inference techniques to exploit this phenomenon. These techniques achieve significant performance gains by modeling observed correlations among class labels of related instances, but the models fail to capture a frequent cause of autocorrelation---the presence of underlying groups that influence the attributes on a set of entities. We propose a latent group model (LGM) for relational data, which discovers and exploits the hidden structures responsible for the observed autocorrelation among class labels. Modeling the latent group structure improves model performance, increases inference efficiency, and enhances our understanding of the datasets. We evaluate performance on three relational classification tasks and show that LGM outperforms models that ignore latent group structure when there is little known information with which to seed inference. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Jennifer Neville and David Jensen 2006 Is Part Of Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.05051.10 urn:nbn:de:0030-drops-4201 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.10 eng https://creativecommons.org/licenses/by/4.0/legalcode