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
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Machine Learning Approach to Building Domain-Specific Search Engines
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A Visual Query Language for Relational Knowledge Discovery TITLE2:
A Visual Query Language for Relational Knowledge Discovery TITLE2:
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and 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
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Stacked dependency networks for layout document structuring
Proceedings of the 2008 ACM symposium on Applied computing
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Structured machine learning: the next ten years
Machine Learning
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Heterogeneous source consensus learning via decision propagation and negotiation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Modelling Screening Mammography Images: A Probabilistic Relational Approach
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting
DS '09 Proceedings of the 12th International Conference on Discovery Science
Scalable statistical learning: a modular Bayesian/Markov network approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Cautious Collective Classification
The Journal of Machine Learning Research
ERACER: a database approach for statistical inference and data cleaning
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Multi-network fusion for collective inference
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Graph regularized transductive classification on heterogeneous information networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Label-dependent feature extraction in social networks for node classification
SocInfo'10 Proceedings of the Second international conference on Social informatics
Exploiting statistical and relational information on the web and in social media
Proceedings of the fourth ACM international conference on Web search and data mining
A method of label-dependent feature extraction in social networks
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Integrating knowledge capture and supervised learning through a human-computer interface
Proceedings of the sixth international conference on Knowledge capture
Collective graph identification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking-based classification of heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Network regression with predictive clustering trees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Learning compact markov logic networks with decision trees
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
SmartShadow-K: an practical knowledge network for joint context inference in everyday life
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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
Collective inference for network data with copula latent markov networks
Proceedings of the sixth ACM international conference on Web search and data mining
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Automated probabilistic modeling for relational data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Tabular: a schema-driven probabilistic programming language
Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
Cyclic causal models with discrete variables: Markov Chain equilibrium semantics and sample ordering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A few good predictions: selective node labeling in a social network
Proceedings of the 7th ACM international conference on Web search and data mining
Social network analysis for customer churn prediction
Applied Soft Computing
Single network relational transductive learning
Journal of Artificial Intelligence Research
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Recent work on graphical models for relational data has demonstrated significant improvements in classification and inference when models represent the dependencies among instances. Despite its use in conventional statistical models, the assumption of instance independence is contradicted by most relational data sets. For example, in citation data there are dependencies among the topics of a paper's references, and in genomic data there are dependencies among the functions of interacting proteins. In this paper, we present relational dependency networks (RDNs), graphical models that are capable of expressing and reasoning with such dependencies in a relational setting. We discuss RDNs in the context of relational Bayes networks and relational Markov networks and outline the relative strengths of RDNs---namely, the ability to represent cyclic dependencies, simple methods for parameter estimation, and efficient structure learning techniques. The strengths of RDNs are due to the use of pseudolikelihood learning techniques, which estimate an efficient approximation of the full joint distribution. We present learned RDNs for a number of real-world data sets and evaluate the models in a prediction context, showing that RDNs identify and exploit cyclic relational dependencies to achieve significant performance gains over conventional conditional models. In addition, we use synthetic data to explore model performance under various relational data characteristics, showing that RDN learning and inference techniques are accurate over a wide range of conditions.