Improving Coarsening Schemes for Hypergraph Partitioning by Exploiting Community Structure

Authors Tobias Heuer, Sebastian Schlag

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Tobias Heuer
Sebastian Schlag

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Tobias Heuer and Sebastian Schlag. Improving Coarsening Schemes for Hypergraph Partitioning by Exploiting Community Structure. In 16th International Symposium on Experimental Algorithms (SEA 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 75, pp. 21:1-21:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


We present an improved coarsening process for multilevel hypergraph partitioning that incorporates global information about the community structure. Community detection is performed via modularity maximization on a bipartite graph representation. The approach is made suitable for different classes of hypergraphs by defining weights for the graph edges that express structural properties of the hypergraph. We integrate our approach into a leading multilevel hypergraph partitioner with strong local search algorithms and perform extensive experiments on a large benchmark set of hypergraphs stemming from application areas such as VLSI design, SAT solving, and scientific computing. Our results indicate that respecting community structure during coarsening not only significantly improves the solutions found by the initial partitioning algorithm, but also consistently improves overall solution quality.
  • multilevel hypergraph partitioning
  • coarsening algorithms
  • community detection


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