Exact algorithms for the minimum s-club partitioning problem

  • Oleksandra Yezerska
  • Foad Mahdavi Pajouh
  • Alexander Veremyev
  • Sergiy Butenko
Computational Biomedicine

Abstract

Graph clustering (partitioning) is a helpful tool in understanding complex systems and analyzing their structure and internal properties. One approach for graph clustering is based on partitioning the graph into cliques. However, clique models are too restrictive and prone to errors given imperfect data. Thus, using clique relaxations instead may provide a more reasonable and applicable partitioning of the graph. An s-club is a distance-based relaxation of a clique and is formally defined as a subset of vertices inducing a subgraph with a diameter of at most s. In this work, we study the minimum s-club partitioning problem, which is to partition the graph into a minimum number of non-overlapping s-club clusters. Integer programming techniques and combinatorial branch-and-bound framework are employed to develop exact algorithms to solve this problem. We also study and compare the computational performance of the proposed algorithms for the special cases of \(s=2\) and \(s=3\) on a test-bed of randomly generated instances and real-life graphs.

Keywords

s-Clubs Partitioning Graph-based clustering Integer programming Combinatorial branch-and-bound 

Notes

Acknowledgements

This material is based upon work supported by the AFRL Mathematical Modeling and Optimization Institute. Partial support by AFOSR under Grant FA8651-14-2-0005, and NSF Grant CMMI-1538493 is also gratefully acknowledged.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Oleksandra Yezerska
    • 1
  • Foad Mahdavi Pajouh
    • 2
  • Alexander Veremyev
    • 3
  • Sergiy Butenko
    • 1
  1. 1.Industrial and Systems EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of Management Science and Information SystemsUniversity of Massachusetts BostonBostonUSA
  3. 3.Department of Industrial Engineering and Management SystemsUniversity of Central FloridaOrlandoUSA

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