NDLI: Leveraging relational autocorrelation with latent group models

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Leveraging relational autocorrelation with latent group models

Content Provider	ACM Digital Library
Author	Jensen, David Neville, Jennifer
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, particularly when there is little information with which to seed inference.
Starting Page	49
Ending Page	55
Page Count	7
File Format	PDF
ISBN	1595932127
DOI	10.1145/1090193.1090201
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-21
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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