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PACE Solver Description: KaPoCE: A Heuristic Cluster Editing Algorithm

Authors Thomas Bläsius, Philipp Fischbeck, Lars Gottesbüren, Michael Hamann, Tobias Heuer, Jonas Spinner, Christopher Weyand, Marcus Wilhelm

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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
Keywords
  • cluster editing
  • local search
  • variable neighborhood search

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Abstract

The cluster editing problem is to transform an input graph into a cluster graph by performing a minimum number of edge editing operations. A cluster graph is a graph where each connected component is a clique. An edit operation can be either adding a new edge or removing an existing edge. In this write-up we outline the core techniques used in the heuristic cluster editing algorithm of the Karlsruhe and Potsdam Cluster Editing (KaPoCE) framework, submitted to the heuristic track of the 2021 PACE challenge.

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Thomas Bläsius, Philipp Fischbeck, Lars Gottesbüren, Michael Hamann, Tobias Heuer, Jonas Spinner, Christopher Weyand, and Marcus Wilhelm. PACE Solver Description: KaPoCE: A Heuristic Cluster Editing Algorithm. In 16th International Symposium on Parameterized and Exact Computation (IPEC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 214, pp. 31:1-31:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.IPEC.2021.31

Author Details

Thomas Bläsius
  • Karlsruhe Institute of Technology, Germany
Philipp Fischbeck
  • Hasso Plattner Institute, Potsdam, Germany
Lars Gottesbüren
  • Karlsruhe Institute of Technology, Germany
Michael Hamann
  • Karlsruhe Institute of Technology, Germany
Tobias Heuer
  • Karlsruhe Institute of Technology, Germany
Jonas Spinner
  • Karlsruhe Institute of Technology, Germany
Christopher Weyand
  • Karlsruhe Institute of Technology, Germany
Marcus Wilhelm
  • Karlsruhe Institute of Technology, Germany

Supplementary Materials

References

  1. Lucas Bastos, Luiz Satoru Ochi, Fábio Protti, Anand Subramanian, Ivan César Martins, and Rian Gabriel S Pinheiro. Efficient Algorithms for Cluster Editing. Journal of Combinatorial Optimization, 31(1):347-371, 2016. Google Scholar
  2. Thomas Bläsius, Philipp Fischbeck, Lars Gottesbüren, Michael Hamann, Tobias Heuer, Jonas Spinner, Christopher Weyand, and Marcus Wilhelm. KaPoCE - An Exact and Heuristic Solver for the Cluster Editing Problem. https://github.com/kittobi1992/cluster_editing/tree/pace-2021, 2021. URL: https://doi.org/10.5281/zenodo.4892524.
  3. Pierre Hansen and Nenad Mladenović. Variable Neighborhood Search. In Handbook of Metaheuristics, pages 145-184. Springer, 2003. Google Scholar
  4. George Karypis and Vipin Kumar. Multilevel k-way Hypergraph Partitioning. Technical Report 98-036, University of Minnesota, 1998. Google Scholar
  5. Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks. Physical Review E, 76(3):036106, 2007. Google Scholar
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