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Time-Evolving Relational Classification and Ensemble Methods

  • Ryan Rossi
  • Jennifer Neville
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)

Abstract

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.

Keywords

Temporal Information Ensemble Method Attribute Weight Prediction Task Window Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ryan Rossi
    • 1
  • Jennifer Neville
    • 1
  1. 1.Purdue UniversityWest LafayetteUSA

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