License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05051.10
URN: urn:nbn:de:0030-drops-4201
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/420/
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Neville, Jennifer ; Jensen, David

Leveraging relational autocorrelation with latent group models

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05051.NevilleJennifer.Paper.420.pdf (0.2 MB)

Abstract

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.

BibTeX - Entry

@InProceedings{neville_et_al:DagSemProc.05051.10,
  author =	{Neville, Jennifer and Jensen, David},
  title =	{{Leveraging relational autocorrelation with latent group models}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2006/420},
  URN =		{urn:nbn:de:0030-drops-4201},
  doi =		{10.4230/DagSemProc.05051.10},
  annote =	{Keywords: Statistical relational learning, probabilistic relational models, latent variable models, autocorrelation, collective inference}
}

Keywords: Statistical relational learning, probabilistic relational models, latent variable models, autocorrelation, collective inference
Collection: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue Date: 2006
Date of publication: 19.01.2006

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