Leveraging relational autocorrelation with latent group models
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Using structure indices for efficient approximation of network properties
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Contextual Dependency Network Models for Link-Based Classification
IEEE Transactions on Knowledge and Data Engineering
Relational Dependency Networks
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
A bias/variance decomposition for models using collective inference
Machine Learning
First-Order Probabilistic Languages: Into the Unknown
Inductive Logic Programming
Generalized Ordering-Search for Learning Directed Probabilistic Logical Models
Inductive Logic Programming
Extending Markov Logic to Model Probability Distributions in Relational Domains
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Classifying networked entities with modularity kernels
Proceedings of the 17th ACM conference on Information and knowledge management
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Structure Learning of Markov Logic Networks through Iterated Local Search
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Learning models of macrobehavior in complex adaptive systems
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Unifying logical and statistical AI
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Leveraging Higher Order Dependencies between Features for Text Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A relational representation for procedural task knowledge
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Structure learning for statistical relational models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Cautious inference in collective classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A higher order collective classifier for detecting andclassifying network events
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Bias/variance analysis for relational domains
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Probabilistic inductive logic programming
Probabilistic inductive logic programming
Probabilistic inductive logic programming
ACM Transactions on Information Systems (TOIS)
Discriminative Markov logic network structure learning based on propositionalization and X2-test
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Robust collective classification with contextual dependency network models
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Generative structure learning for Markov logic networks based on graph of predicates
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A regularization framework in polar coordinates for transductive learning in networked data
Information Sciences: an International Journal
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Instance independence is a critical assumption of traditional machine learning methods contradicted by many relational datasets. For example, in scientific literature datasets there are dependencies among the references of a paper. Recent work on graphical models for relational data has demonstrated significant performance gains for models that exploit the dependencies among instances. In this paper, we present relational dependency networks (RDNs), a new form of graphical model capable of reasoning with such dependencies in a relational setting. We describe the details of RDN models and outline their strengths, most notably the ability to learn and reason with cyclic relational dependencies. We present RDN models learned on a number of real-world datasets, and evaluate the models in a classification context, showing significant performance improvements. In addition, we use synthetic data to evaluate the quality of model learning and inference procedures.