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Solving Edge Clique Cover Exactly via Synergistic Data Reduction

Authors Anthony Hevia , Benjamin Kallus, Summer McClintic , Samantha Reisner, Darren Strash , Johnathan Wilson

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Author Details

Anthony Hevia
  • Hamilton College, Clinton, NY, USA
Benjamin Kallus
  • Dartmouth College, Hanover, NH, USA
Summer McClintic
  • Hamilton College, Clinton, NY, USA
Samantha Reisner
  • Hamilton College, Clinton, NY, USA
Darren Strash
  • Hamilton College, Clinton, NY, USA
Johnathan Wilson
  • Hamilton College, Clinton, NY, USA


We thank the anonymous reviewers for their insightful feedback, David Swartz from Hamilton College for technical support, and Adam Chrisman, Caitlin Matwijec-Walda, and Jon Matwijec-Walda for a cozy space to work at The Copper Easel and Superofficial in Rome, NY.

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Anthony Hevia, Benjamin Kallus, Summer McClintic, Samantha Reisner, Darren Strash, and Johnathan Wilson. Solving Edge Clique Cover Exactly via Synergistic Data Reduction. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 61:1-61:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


The edge clique cover (ECC) problem - where the goal is to find a minimum cardinality set of cliques that cover all the edges of a graph - is a classic NP-hard problem that has received much attention from both the theoretical and experimental algorithms communities. While small sparse graphs can be solved exactly via the branch-and-reduce algorithm of Gramm et al. [JEA 2009], larger instances can currently only be solved inexactly using heuristics with unknown overall solution quality. We revisit computing minimum ECCs exactly in practice by combining data reduction for both the ECC and vertex clique cover (VCC) problems. We do so by modifying the polynomial-time reduction of Kou et al. [Commun. ACM 1978] to transform a reduced ECC instance to a VCC instance; alternatively, we show it is possible to "lift" some VCC reductions to the ECC problem. Our experiments show that combining data reduction for both problems (which we call synergistic data reduction) enables finding exact minimum ECCs orders of magnitude faster than the technique of Gramm et al., and allows solving large sparse graphs on up to millions of vertices and edges that have never before been solved. With these new exact solutions, we evaluate the quality of recent heuristic algorithms on large instances for the first time. The most recent of these, EO-ECC by Abdullah et al. [ICCS 2022], solves 8 of the 27 instances for which we have exact solutions. It is our hope that our strategy rallies researchers to seek improved algorithms for the ECC problem.

Subject Classification

ACM Subject Classification
  • Theory of computation → Graph algorithms analysis
  • Mathematics of computing → Graph algorithms
  • Theory of computation → Parameterized complexity and exact algorithms
  • Theory of computation → Packing and covering problems
  • Edge clique cover
  • Vertex clique cover
  • Data reduction
  • Degeneracy


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