The PACE 2021 Parameterized Algorithms and Computational Experiments Challenge: Cluster Editing

Authors Leon Kellerhals , Tomohiro Koana , André Nichterlein , Philipp Zschoche



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Author Details

Leon Kellerhals
  • Algorithmics and Computational Complexity, Faculty IV, Technische Universität Berlin, Germany
Tomohiro Koana
  • Algorithmics and Computational Complexity, Faculty IV, Technische Universität Berlin, Germany
André Nichterlein
  • Algorithmics and Computational Complexity, Faculty IV, Technische Universität Berlin, Germany
Philipp Zschoche
  • Algorithmics and Computational Complexity, Faculty IV, Technische Universität Berlin, Germany

Acknowledgements

The PACE challenge was supported by Networks and Technische Universität Berlin. The prize money (€4000) was generously provided by the Networks, an NWO Gravitation project of the University of Amsterdam, Eindhoven University of Technology, Leiden University and the Center for Mathematics and Computer Science (CWI). We are grateful to the whole optil.io team, led by Szymon Wasik, and especially to Jan Badura and Artur Laskowski for the fruitful collaboration and for hosting the competition at the optil.io online judge system. We thank Aleksander Figiel (Technische Universität Berlin) for supporting us with scripts in the data collection process.

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Leon Kellerhals, Tomohiro Koana, André Nichterlein, and Philipp Zschoche. The PACE 2021 Parameterized Algorithms and Computational Experiments Challenge: Cluster Editing. In 16th International Symposium on Parameterized and Exact Computation (IPEC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 214, pp. 26:1-26:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.IPEC.2021.26

Abstract

The Parameterized Algorithms and Computational Experiments challenge (PACE) 2021 was devoted to engineer algorithms solving the NP-hard Cluster Editing problem, also known as Correlation Clustering: Given an undirected graph the task is to compute a minimum number of edges to insert or remove in a way that the resulting graph is a cluster graph, that is, a graph in which each connected component is a clique. Altogether 67 participants from 21 teams, 11 countries, and 3 continents submitted their implementations to the competition. In this report, we describe the setup of the challenge, the selection of benchmark instances, and the ranking of the participating teams. We also briefly discuss the approaches used in the submitted solvers.

Subject Classification

ACM Subject Classification
  • Theory of computation → Graph algorithms analysis
  • Theory of computation → Parameterized complexity and exact algorithms
Keywords
  • Correlation Clustering
  • Cluster Editing
  • Algorithm Engineering
  • FPT
  • Kernelization
  • Heuristics

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