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A Parallel Framework for Approximate Max-Dicut in Partitionable Graphs

Authors Nico Bertram, Jonas Ellert , Johannes Fischer

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Author Details

Nico Bertram
  • Department of Computer Science, Technische Universität Dortmund, Germany
Jonas Ellert
  • Department of Computer Science, Technische Universität Dortmund, Germany
Johannes Fischer
  • Department of Computer Science, Technische Universität Dortmund, Germany

Funding

This work has been supported by the German Research Association (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis”, project A6.

Cite As Get BibTex

Nico Bertram, Jonas Ellert, and Johannes Fischer. A Parallel Framework for Approximate Max-Dicut in Partitionable Graphs. In 20th International Symposium on Experimental Algorithms (SEA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 233, pp. 10:1-10:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.SEA.2022.10

Abstract

Computing a maximum cut in undirected and weighted graphs is a well studied problem and has many practical solutions that also scale well in shared memory (despite its NP-completeness). For its counterpart in directed graphs, however, we are not aware of practical solutions that also utilize parallelism. We engineer a framework that computes a high quality approximate cut in directed and weighted graphs by using a graph partitioning approach. The general idea is to partition a graph into k subgraphs using a parallel partitioning algorithm of our choice (the first ingredient of our framework). Then, for each subgraph in parallel, we compute a cut using any polynomial time approximation algorithm (the second ingredient). In a final step, we merge the locally computed solutions using a high-quality or exact parallel Max-Dicut algorithm (the third ingredient). On graphs that can be partitioned well, the quality of the computed cut is significantly better than the best cut achieved by any linear time algorithm. This is particularly relevant for large graphs, where linear time algorithms used to be the only feasible option.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
  • Theory of computation → Design and analysis of algorithms
Keywords
  • maximum directed cut
  • graph partitioning
  • algorithm engineering
  • approximation
  • parallel algorithms

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References

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