Leveraging Relational Autocorrelation with Latent Group Models Export

@inproceedings{citeulike:394156, 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.}, author = {Neville, Jennifer and Jensen, David}, booktitle = {Proceedings of the 4th Multi-Relational Data Mining Workshop, 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, citeulike-article-id = {394156}, citeulike-linkout-0 = {http://kdl.cs.umass.edu/papers/neville-jensen-mrdm2005.pdf}, comment = {Similar to http://www.citeulike.org/user/ldietz/article/397080}, keywords = {groupmodel, socialnets, topicinference}, posted-at = {2005-11-15 16:03:11}, priority = {2}, title = {Leveraging Relational Autocorrelation with Latent Group Models}, url = {http://kdl.cs.umass.edu/papers/neville-jensen-mrdm2005.pdf}, year = {2005} }

TY - CONF ID - citeulike:394156 L3 - citeulike-article-id:394156 N2 - 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. TI - Leveraging Relational Autocorrelation with Latent Group Models T2 - Proceedings of the 4th Multi-Relational Data Mining Workshop, 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KW - groupmodel KW - socialnets KW - topicinference N1 - Similar to http://www.citeulike.org/user/ldietz/article/397080 AU - Neville, Jennifer AU - Jensen, David PY - 2005/// UR - http://kdl.cs.umass.edu/papers/neville-jensen-mrdm2005.pdf ER -