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A derandomized approximation algorithm for the critical node detection problem
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DOI：10.1016/j.cor.2013.09.012.png)
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Abstract
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In this paper we propose an efficient approximation algorithm for determining solutions to the critical node detection problem (CNDP) on unweighted and undirected graphs. Given a user-defined number of vertices k > 0, the problem is to determine which k nodes to remove such as to minimize pairwise connectivity in the induced subgraph. We present a simple, yet powerful, algorithm that is derived from a randomized rounding of the relaxed linear programming solution to the CNDP. We prove that the expected solution quality obtained by the linear-time algorithm is bounded by a constant. To highlight the algorithm quality four common complex network models are utilized, in addition to four real-world networks. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Critical node detection problem
Randomized rounding
Complex network
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