Leveraging relational autocorrelation with latent group models
Open accessAbstract
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.
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Self-citation
- Authors
- Neville, J., & Jensen, D.
- Affiliations
- University of Massachusetts Amherst (US)
- Venue
- Proceedings of the 4th international workshop on Multi-relational mining - MRDM '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 49-55
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W1943227780
- Cited by
- 121
- References
- 12
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 1 · 2017: 3 · 2016: 7 · 2015: 5 · 2014: 11 · 2013: 7 · 2012: 9
- FWCI
- 6.66
- Type
- conference paper
- Language
- en
- Open access
- green
- drops.dagstuhl.de
- Locations
- Proceedings (publishedVersion)
- DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) (submittedVersion, cc-by)
- ScholarWorks@UMassAmherst (University of Massachusetts Amherst) (submittedVersion)
- Scholarworks (University of Massachusetts Amherst) (submittedVersion)
- DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) (cc-by)
- Dagstuhl Seminar Proceedings
- Funders
- National Science Foundation
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- relational learning, collective inference, relational classification, latent group structure, relational data
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