License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SEA.2021.8
URN: urn:nbn:de:0030-drops-137802
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Heuer, Tobias ; Maas, Nikolai ; Schlag, Sebastian

Multilevel Hypergraph Partitioning with Vertex Weights Revisited

LIPIcs-SEA-2021-8.pdf (2 MB)


The balanced hypergraph partitioning problem (HGP) is to partition the vertex set of a hypergraph into k disjoint blocks of bounded weight, while minimizing an objective function defined on the hyperedges. Whereas real-world applications often use vertex and edge weights to accurately model the underlying problem, the HGP research community commonly works with unweighted instances.
In this paper, we argue that, in the presence of vertex weights, current balance constraint definitions either yield infeasible partitioning problems or allow unnecessarily large imbalances and propose a new definition that overcomes these problems. We show that state-of-the-art hypergraph partitioners often struggle considerably with weighted instances and tight balance constraints (even with our new balance definition). Thus, we present a recursive-bipartitioning technique that is able to reliably compute balanced (and hence feasible) solutions. The proposed method balances the partition by pre-assigning a small subset of the heaviest vertices to the two blocks of each bipartition (using an algorithm originally developed for the job scheduling problem) and optimizes the actual partitioning objective on the remaining vertices. We integrate our algorithm into the multilevel hypergraph partitioner KaHyPar and show that our approach is able to compute balanced partitions of high quality on a diverse set of benchmark instances.

BibTeX - Entry

  author =	{Heuer, Tobias and Maas, Nikolai and Schlag, Sebastian},
  title =	{{Multilevel Hypergraph Partitioning with Vertex Weights Revisited}},
  booktitle =	{19th International Symposium on Experimental Algorithms (SEA 2021)},
  pages =	{8:1--8:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-185-6},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{190},
  editor =	{Coudert, David and Natale, Emanuele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-137802},
  doi =		{10.4230/LIPIcs.SEA.2021.8},
  annote =	{Keywords: multilevel hypergraph partitioning, balanced partitioning, vertex weights}

Keywords: multilevel hypergraph partitioning, balanced partitioning, vertex weights
Collection: 19th International Symposium on Experimental Algorithms (SEA 2021)
Issue Date: 2021
Date of publication: 31.05.2021
Supplementary Material: Software (Source Code): archived at:
Dataset (Benchmark Set & Experimental Results):

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