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Detecting large risk-averse 2-clubs in graphs with random edge failures

Author

Listed:
  • Foad Mahdavi Pajouh

    (University of Massachusetts Boston)

  • Esmaeel Moradi

    (Lee Scott Logistics Complex)

  • Balabhaskar Balasundaram

    (Oklahoma State University)

Abstract

Detecting large 2-clubs in biological, social and financial networks can help reveal important information about the structure of the underlying systems. In large-scale networks that are error-prone, the uncertainty associated with the existence of an edge between two vertices can be modeled by assigning a failure probability to that edge. Here, we study the problem of detecting large “risk-averse” 2-clubs in graphs subject to probabilistic edge failures. To achieve risk aversion, we first model the loss in 2-club property due to probabilistic edge failures as a function of the decision (chosen 2-club cluster) and randomness (graph structure). Then, we utilize the conditional value-at-risk (CVaR) of the loss for a given decision as a quantitative measure of risk for that decision, which is bounded in the model. More precisely, the problem is modeled as a CVaR-constrained single-stage stochastic program. The main contribution of this article is a new Benders decomposition algorithm that outperforms an existing decomposition approach on a test-bed of randomly generated instances, and real-life biological and social networks.

Suggested Citation

  • Foad Mahdavi Pajouh & Esmaeel Moradi & Balabhaskar Balasundaram, 2017. "Detecting large risk-averse 2-clubs in graphs with random edge failures," Annals of Operations Research, Springer, vol. 249(1), pages 55-73, February.
    • Handle: RePEc:spr:annopr:v:249:y:2017:i:1:d:10.1007_s10479-016-2279-0
    DOI: 10.1007/s10479-016-2279-0
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    References listed on IDEAS

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