On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
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
Relational Ensemble Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Ensembles of relational classifiers
Knowledge and Information Systems
Spatiotemporal Relational Probability Trees: An Introduction
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Modeling the evolution of discussion topics and communication to improve relational classification
Proceedings of the First Workshop on Social Media Analytics
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Role-dynamics: fast mining of large dynamic networks
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Modeling dynamic behavior in large evolving graphs
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Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Using social network knowledge for detecting spider constructions in social security fraud
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Relational networks often evolve over time by the addition, deletion, and changing of links, nodes, and attributes. However, accurately incorporating the full range of temporal dependencies into relational learning algorithms remains a challenge. We propose a novel framework for discovering temporal-relational representations for classification. The framework considers transformations over all the evolving relational components (attributes, edges, and nodes) in order to accurately incorporate temporal dependencies into relational models. Additionally, we propose temporal ensemble methods and demonstrate their effectiveness against traditional and relational ensembles on two real-world datasets. In all cases, the proposed temporal-relational models outperform competing models that ignore temporal information.