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Programming and Computer Software

, Volume 41, Issue 5, pp 302–306 | Cite as

Improving quality of graph partitioning using multi-level optimization

  • R. K. PastukhovEmail author
  • A. V. Korshunov
  • D. Yu. Turdakov
  • S. D. Kuznetsov
Image Processing

Abstract

Graph partitioning is required for solving tasks on graphs that need to be distributed over disks or computers. This problem is well studied, but the majority of the results on this subject are not suitable for processing graphs with billions of nodes on commodity clusters, since they require shared memory or lowlatency messaging. One of the approaches suitable for cluster computing is the balanced label propagation, which is based on the label propagation algorithm. In this work, we show how multi-level optimization can be used to improve quality of the partitioning obtained by means of the balanced label propagation algorithm.

Keywords

Computer Node Multi Level Initial Partitioning Social Graph Edge Contraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Pleiades Publishing, Ltd. 2015

Authors and Affiliations

  • R. K. Pastukhov
    • 1
    Email author
  • A. V. Korshunov
    • 1
  • D. Yu. Turdakov
    • 1
  • S. D. Kuznetsov
    • 1
  1. 1.Institute for System ProgrammingRussian Academy of SciencesMoscowRussia

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