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Clique Relaxation Models in Social Network Analysis

In: Handbook of Optimization in Complex Networks

Author

Listed:
  • Jeffrey Pattillo

    (Texas A&M University)

  • Nataly Youssef

    (Texas A&M University)

  • Sergiy Butenko

    (Texas A&M University)

Abstract

Clique relaxation models that were originally introduced in the literature on social network analysis are not only gaining increasing popularity in a wide spectrum of complex network applications, but also keep garnering attention of mathematicians, computer scientists, and operations researchers as a promising avenue for fruitful theoretical investigations. This chapter describes the origins of clique relaxation concepts and provides a brief overview of mathematical programming formulations for the corresponding optimization problems, algorithms proposed to solve these problems, and selected real-life applications of the models of interest.

Suggested Citation

  • Jeffrey Pattillo & Nataly Youssef & Sergiy Butenko, 2012. "Clique Relaxation Models in Social Network Analysis," Springer Optimization and Its Applications, in: My T. Thai & Panos M. Pardalos (ed.), Handbook of Optimization in Complex Networks, chapter 0, pages 143-162, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-0857-4_5
    DOI: 10.1007/978-1-4614-0857-4_5
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    Citations

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    Cited by:

    1. Zhuqi Miao & Balabhaskar Balasundaram & Eduardo L. Pasiliao, 2014. "An exact algorithm for the maximum probabilistic clique problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 105-120, July.
    2. Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Illya V. Hicks, 2016. "On the 2-Club Polytope of Graphs," Operations Research, INFORMS, vol. 64(6), pages 1466-1481, December.
    3. Wu, Qinghua & Hao, Jin-Kao, 2015. "A review on algorithms for maximum clique problems," European Journal of Operational Research, Elsevier, vol. 242(3), pages 693-709.
    4. Zhou, Yi & Lin, Weibo & Hao, Jin-Kao & Xiao, Mingyu & Jin, Yan, 2022. "An effective branch-and-bound algorithm for the maximum s-bundle problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 27-39.
    5. Timo Gschwind & Stefan Irnich & Fabio Furini & Roberto Wolfler Calvo, 2017. "Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques," Working Papers 1722, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    6. Assif Assad & Kusum Deep, 2018. "A heuristic based harmony search algorithm for maximum clique problem," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 411-433, June.

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