Partial Kernelization for Rank Aggregation: Theory and Experiments

  • Nadja Betzler
  • Robert Bredereck
  • Rolf Niedermeier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6478)


Rank Aggregation is important in many areas ranging from web search over databases to bioinformatics. The underlying decision problem Kemeny Score is NP-complete even in case of four input rankings to be aggregated into a “median ranking”. We study efficient polynomial-time data reduction rules that allow us to find optimal median rankings. On the theoretical side, we improve a result for a “partial problem kernel” from quadratic to linear size. On the practical side, we provide encouraging experimental results with data based on web search and sport competitions, e.g., computing optimal median rankings for real-world instances with more than 100 candidates within milliseconds.


Dynamic Programming Algorithm Reduction Rule Condorcet Winner Candidate Pair Rank Aggregation 
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 2010

Authors and Affiliations

  • Nadja Betzler
    • 1
  • Robert Bredereck
    • 1
  • Rolf Niedermeier
    • 1
  1. 1.Institut für InformatikFriedrich-Schiller-Universität JenaJenaGermany

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