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Vertex Downgrading to Minimize Connectivity

Authors Hassene Aissi, Da Qi Chen, R. Ravi

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ACM Subject Classification
  • Theory of computation → Routing and network design problems
Keywords
  • Vertex Interdiction
  • Vertex Downgrading
  • Network Interdiction
  • Approximation Algorithm

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Abstract

We consider the problem of interdicting a directed graph by deleting nodes with the goal of minimizing the local edge connectivity of the remaining graph from a given source to a sink. We introduce and study a general downgrading variant of the interdiction problem where the capacity of an arc is a function of the subset of its endpoints that are downgraded, and the goal is to minimize the downgraded capacity of a minimum source-sink cut subject to a node downgrading budget. This models the case when both ends of an arc must be downgraded to remove it, for example. For this generalization, we provide a bicriteria (4,4)-approximation that downgrades nodes with total weight at most 4 times the budget and provides a solution where the downgraded connectivity from the source to the sink is at most 4 times that in an optimal solution. We accomplish this with an LP relaxation and rounding using a ball-growing algorithm based on the LP values. We further generalize the downgrading problem to one where each vertex can be downgraded to one of k levels, and the arc capacities are functions of the pairs of levels to which its ends are downgraded. We generalize our LP rounding to get a (4k,4k)-approximation for this case.

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Hassene Aissi, Da Qi Chen, and R. Ravi. Vertex Downgrading to Minimize Connectivity. In 17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 162, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.SWAT.2020.5

Author Details

Hassene Aissi
  • Paris Dauphine University, France
Da Qi Chen
  • Carnegie Mellon University, Pittsburgh, PA, USA
R. Ravi
  • Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA

Funding

  • Aissi, Hassene: This research benefited from the support of the FMJH Program PGMO and from the support of EDF, Thales, Orange et Criteo.
  • Ravi, R.: This material is based upon research supported in part by the U. S. Office of Naval Research under award number N00014-18-1-2099.

References

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