## 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(logn)" role="presentation">O(logn).

Original language | Undefined/Unknown |
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Title of host publication | Algorithms for Computational Biology : 5th International Conference, AlCoB 2018, Hong Kong, China, June 25–26, 2018, Proceedings |

Editors | Jansson Jesper, Martin-Vide Carlos, Vega-Rodriguez Miguel |

Publisher | Springer |

Pages | 103–114 |

ISBN (Electronic) | 978-3-319-91938-6 |

ISBN (Print) | 978-3-319-91937-9 |

DOIs | |

Publication status | Published - 2018 |

MoE publication type | A4 Article in a conference publication |

Event | International Conference on Algorithms for Computational Biology (AlCoB) - 5th International Conference on Algorithms for Computational Biology, AlCoB Duration: 25 Jun 2018 → 26 Jun 2018 |

### Conference

Conference | International Conference on Algorithms for Computational Biology (AlCoB) |
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Period | 25/06/18 → 26/06/18 |

## Keywords

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