Fixed Parameter Algorithms and Hardness of Approximation Results for the Structural Target Controllability Problem

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Abstract

Recent research has revealed new applications of network control science within bio-medicine, pharmacology, and medical therapeutics. These new insights and new applications generated in turn a rediscovery of some old, unresolved algorithmic questions, this time with a much stronger motivation for their tackling. One of these questions regards the so-called Structural Target Control optimization problem, known in previous literature also as Structural Output Controllability problem. Given a directed network (graph) and a target subset of nodes, the task is to select a small (or the smallest) set of nodes from which the target can be independently controlled, i.e., it can be driven from any given initial configuration to any desired final one, through a finite sequence of input values. In recent work, this problem has been shown to be NP-hard, and several heuristic algorithms were introduced and analyzed, both on randomly generated networks, and on bio-medical ones. In this paper, we show that the Structural Target Controllability problem is fixed parameter tractable when parameterized by the number of target nodes. We also prove that the problem is hard to approximate at a factor better than O(log⁡n)" role="presentation">O(logn).

Original languageUndefined/Unknown
Title of host publicationAlgorithms for Computational Biology : 5th International Conference, AlCoB 2018, Hong Kong, China, June 25–26, 2018, Proceedings
EditorsJansson Jesper, Martin-Vide Carlos, Vega-Rodriguez Miguel
PublisherSpringer
Pages103–114
ISBN (Electronic)978-3-319-91938-6
ISBN (Print)978-3-319-91937-9
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Algorithms for Computational Biology (AlCoB) - 5th International Conference on Algorithms for Computational Biology, AlCoB
Duration: 25 Jun 201826 Jun 2018

Conference

ConferenceInternational Conference on Algorithms for Computational Biology (AlCoB)
Period25/06/1826/06/18

Keywords

  • Cancer
  • Efficient algorithms
  • Graph-search algorithms
  • computational complexity

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