Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Discovering Test Set Regularities in Relational Domains
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
The Journal of Machine Learning Research
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster-based concept invention for statistical relational learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A machine learning approach to building domain-specific search engines
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
ACM SIGKDD Explorations Newsletter
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Statistical predicate invention
Proceedings of the 24th international conference on Machine learning
Finding tribes: identifying close-knit individuals from employment patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A bias/variance decomposition for models using collective inference
Machine Learning
Proceedings of the 18th international conference on World wide web
Measuring the effects of preprocessing decisions and network forces in dynamic network analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning models of macrobehavior in complex adaptive systems
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Case-Based Collective Inference for Maritime Object Classification
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Cautious inference in collective classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Data clustering with a relational push-pull model
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The relational push-pull model: a generative model for relational data clustering
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Cautious Collective Classification
The Journal of Machine Learning Research
Bias/variance analysis for relational domains
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
A community-based pseudolikelihood approach for relationship labeling in social networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Reference classes and relational learning
International Journal of Approximate Reasoning
An analysis of how ensembles of collective classifiers improve predictions in graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
Enhanced spatiotemporal relational probability trees and forests
Data Mining and Knowledge Discovery
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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The presence of autocorrelation provides a strong motivation for using relational learning and inference techniques. Autocorrelation is a statistical dependence 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 thatinfluence 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.